Experiment with Random Difficulty (results based on the bet)

Removing Outliers

## [1] "Outliers : 3qq8dp8jk, 79pn8m6v8, e58u3sinl, hudayxdge, w2x28nknu"

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers : "

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers : "

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Total number of outliers:  5"

## [1] "Total number of outliers motor task:  1"

## [1] "Total number of outliers perceptive task:  1"

## [1] "Total number of outliers logical task:  3"

Modeling objective difficulty for motor task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   2268.1   2290.2  -1130.0   2260.1     1877 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.9846 -0.7313  0.2308  0.7546  2.8895 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.5178   0.7196  
## Number of obs: 1881, groups:  IDjoueur, 57
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.1555     0.1548  -7.464 8.42e-14 ***
## difficulty    3.0512     0.2019  15.113  < 2e-16 ***
## timeNorm     -0.3871     0.1728  -2.241   0.0251 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.488       
## timeNorm   -0.430 -0.167

## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.

## 
##  Logique2   Motrice Sensoriel 
##         0      1881         0 
## [1] "Player levels from ranef:"
##   (Intercept)        
##  Min.   :-0.9973563  
##  1st Qu.:-0.4243437  
##  Median :-0.1362009  
##  Mean   :-0.0003041  
##  3rd Qu.: 0.3781255  
##  Max.   : 1.6570924  
## [1] "Intercept: -1.16 8.4e-14 ***"
## [1] "Difficulty: 3.05 1.3e-51 ***"
## [1] "Time: -0.387 0.025 *"
## [1] "R2 fixed: 0.16"
## [1] "R2 mixed: 0.28"
## [1] "Cross Val: 0.68"
## [1] "AIC: 2300"

##         0%        25%        50%        75%       100% 
## -1.6570924 -0.3781255  0.1362009  0.4243437  0.9973563

##         0%        25%        50%        75%       100% 
## -1.6570924 -0.3781255  0.1362009  0.4243437  0.9973563

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for sensory task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1535.4   1557.4   -763.7   1527.4     1811 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.2914 -0.4479  0.1164  0.3982  4.7670 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.5772   0.7598  
## Number of obs: 1815, groups:  IDjoueur, 55
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -2.2311     0.1826 -12.217  < 2e-16 ***
## difficulty    7.0302     0.3250  21.631  < 2e-16 ***
## timeNorm     -1.0832     0.2369  -4.572 4.84e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.458       
## timeNorm   -0.385 -0.358

## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.

## 
##  Logique2   Motrice Sensoriel 
##         0         0      1815 
## [1] "Player levels from ranef:"
##   (Intercept)       
##  Min.   :-1.527169  
##  1st Qu.:-0.388916  
##  Median :-0.005975  
##  Mean   : 0.002363  
##  3rd Qu.: 0.374680  
##  Max.   : 1.350107  
## [1] "Intercept: -2.23 2.5e-34 ***"
## [1] "Difficulty: 7.03 9.1e-104 ***"
## [1] "Time: -1.08 4.8e-06 ***"
## [1] "R2 fixed: 0.55"
## [1] "R2 mixed: 0.62"
## [1] "Cross Val: 0.8"
## [1] "AIC: 1500"

##           0%          25%          50%          75%         100% 
## -1.350106912 -0.374679910  0.005974618  0.388915836  1.527169116

##           0%          25%          50%          75%         100% 
## -1.350106912 -0.374679910  0.005974618  0.388915836  1.527169116

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for logical task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1816.7   1838.8   -904.3   1808.7     1877 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.7618 -0.5239 -0.1972  0.5160  5.0573 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 1.067    1.033   
## Number of obs: 1881, groups:  IDjoueur, 57
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.6536     0.1924  -8.596   <2e-16 ***
## difficulty    5.4305     0.2647  20.515   <2e-16 ***
## timeNorm     -2.0774     0.2224  -9.340   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.388       
## timeNorm   -0.276 -0.437

## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.

## 
##  Logique2   Motrice Sensoriel 
##      1881         0         0 
## [1] "Player levels from ranef:"
##   (Intercept)       
##  Min.   :-1.492039  
##  1st Qu.:-0.741161  
##  Median :-0.213560  
##  Mean   : 0.004668  
##  3rd Qu.: 0.599760  
##  Max.   : 2.373359  
## [1] "Intercept: -1.65 8.2e-18 ***"
## [1] "Difficulty: 5.43 1.6e-93 ***"
## [1] "Time: -2.08 9.6e-21 ***"
## [1] "R2 fixed: 0.38"
## [1] "R2 mixed: 0.53"
## [1] "Cross Val: 0.79"
## [1] "AIC: 1800"

##         0%        25%        50%        75%       100% 
## -2.3733594 -0.5997602  0.2135598  0.7411607  1.4920388

##         0%        25%        50%        75%       100% 
## -2.3733594 -0.5997602  0.2135598  0.7411607  1.4920388

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Influence of Player Profiles

Player profiles

Influence of Player Profiles

Objective level and player profile

Playing video games in general and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.0832, p-value = 0.2787
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1121498

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.27984, p-value = 0.7796
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.02959975

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.18429, p-value = 0.8538
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.01913758

Playing board games in general and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.92279, p-value = 0.3561
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.09432639

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.40055, p-value = 0.6887
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.04164333

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.83074, p-value = 0.4061
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.08524489

Self efficacy and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning: Removed 29 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.17852, p-value = 0.8583
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.02429648

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning: Removed 27 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.5588, p-value = 0.0105
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.3482495 
## 
## [1] "self.eff.on.level.s 0.35 0.011 *"

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning: Removed 28 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.77294, p-value = 0.4396
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1034345

Risk aversion and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.2531, p-value = 0.2102
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1232133

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.9255, p-value = 0.05417
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1918732 
## 
## [1] "risk.av.on.level.s 0.19 0.054 ."

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.0617, p-value = 0.2884
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1042971

Age and level for each task

## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.0129, p-value = 0.3111
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.09643322

## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.0949, p-value = 0.03618
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2036664 
## 
## [1] "age.on.level.s 0.2 0.036 *"

## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.2495, p-value = 0.2115
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1192254

Sex and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.3361, p-value = 0.01949
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## -0.257113 
## 
## [1] "sexe.on.level.m -0.26 0.019 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0, p-value = 1
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau 
##   0

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.18884, p-value = 0.8502
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.02078441

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 223, p-value = 0.01897
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.85282846 -0.09534056
## sample estimates:
## difference in location 
##             -0.5051082 
## 
## [1] "sexe.on.level.m.2 -0.51 0.019 * mean(A): 0.16 mean(B): -0.32"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 333, p-value = 1
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.3670949  0.4731302
## sample estimates:
## difference in location 
##          -0.0009246191

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 340, p-value = 0.8583
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.7335260  0.5047401
## sample estimates:
## difference in location 
##            -0.02802612

Subjective difficulty and play habits

Playing video game in general and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.62185, p-value = 0.534
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.03720939

Playing board game in general and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -3.4464, p-value = 0.0005681
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2033235 
## 
## [1] "pbg.on.error -0.2 0.00057 ***"

In game level and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.44873, p-value = 0.6536
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.02338143

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.23405, p-value = 0.8149
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.02130326

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.094374, p-value = 0.9248
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##          tau 
## -0.008754209

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.45433, p-value = 0.6496
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.04135338

Sex and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 4.1645, p-value = 3.12e-05
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2646112 
## 
## [1] "sexe.on.error 0.26 3.1e-05 ***"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.3699, p-value = 0.01779
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2608393 
## 
## [1] "sexe.on.error.m 0.26 0.018 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.565, p-value = 0.01032
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2875846 
## 
## [1] "sexe.on.error.s 0.29 0.01 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.2318, p-value = 0.02563
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2456339 
## 
## [1] "sexe.on.error.l 0.25 0.026 *"

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  B and A
## W = 4376, p-value = 3.143e-05
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.04977679 0.13237866
## sample estimates:
## difference in location 
##             0.09299933 
## 
## [1] "sexe.on.error.2 0.093 3.1e-05 *** mean(A): -0.093 mean(B): 0.001"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 501, p-value = 0.01724
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.01355287 0.15331497
## sample estimates:
## difference in location 
##             0.09290042 
## 
## [1] "sexe.on.error.m.2 0.093 0.017 * mean(A): -0.085 mean(B): 0.0073"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 476, p-value = 0.009655
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.02092227 0.15744127
## sample estimates:
## difference in location 
##             0.09796631 
## 
## [1] "sexe.on.error.s.2 0.098 0.0097 ** mean(A): -0.1 mean(B): -0.0014"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 481, p-value = 0.02523
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.009389481 0.150561466
## sample estimates:
## difference in location 
##             0.09060751 
## 
## [1] "sexe.on.error.l.2 0.091 0.025 * mean(A): -0.091 mean(B): -0.0033"

Risk aversion and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.60676, p-value = 0.544
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.03431688

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.12035, p-value = 0.9042
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.01183404

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.11152, p-value = 0.9112
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.01111235

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.79275, p-value = 0.4279
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.07787518

Self efficacy and subjective difficulty error

## Warning: Removed 84 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.9644, p-value = 0.003033
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2277125 
## 
## [1] "self.eff.on.error -0.23 0.003 **"

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning: Removed 29 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.7653, p-value = 0.07751
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2402652 
## 
## [1] "self.eff.on.error -0.24 0.078 ."

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning: Removed 27 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.6463, p-value = 0.09969
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2240675 
## 
## [1] "self.eff.on.error -0.22 0.1 :("

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning: Removed 28 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.6401, p-value = 0.101
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2194829

Influence of Objective difficulty on Subjective Difficulty

All tasks

## [1] "all"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0096 47     0.64 :(
##  2:      0.09375         0.0440 54     0.052 .
##  3:      0.15625         0.0045 58     0.91 :(
##  4:      0.21875         0.0260 58     0.27 :(
##  5:      0.28125         0.0044 57     0.98 :(
##  6:      0.34375        -0.0400 58     0.25 :(
##  7:      0.40625        -0.0400 58     0.23 :(
##  8:      0.46875        -0.0045 58     0.94 :(
##  9:      0.53125        -0.0190 58     0.54 :(
## 10:      0.59375        -0.0420 58     0.18 :(
## 11:      0.65625        -0.0370 58     0.31 :(
## 12:      0.71875        -0.1100 58 1.9e-05 ***
## 13:      0.78125        -0.1400 58 7.4e-08 ***
## 14:      0.84375        -0.2100 58 1.7e-09 ***
## 15:      0.90625        -0.1900 57   5e-11 ***
## 16:      0.96875        -0.1800 55 1.1e-10 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 47     0.64 :(
##  2: 54     0.052 .
##  3: 58     0.91 :(
##  4: 58     0.27 :(
##  5: 57     0.98 :(
##  6: 58     0.25 :(
##  7: 58     0.23 :(
##  8: 58     0.94 :(
##  9: 58     0.54 :(
## 10: 58     0.18 :(
## 11: 58     0.31 :(
## 12: 58 1.9e-05 ***
## 13: 58 7.4e-08 ***
## 14: 58 1.7e-09 ***
## 15: 57   5e-11 ***
## 16: 55 1.1e-10 ***
## [1] 56.8
## [1] 2.86

## [1] "good"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.027 22     0.21 :(
##  2:      0.09375         -0.022 25     0.68 :(
##  3:      0.15625         -0.085 37     0.066 .
##  4:      0.21875         -0.058 36     0.057 .
##  5:      0.28125         -0.038 34     0.38 :(
##  6:      0.34375         -0.058 35     0.39 :(
##  7:      0.40625         -0.013 32     0.86 :(
##  8:      0.46875          0.031 35     0.53 :(
##  9:      0.53125          0.070 35     0.23 :(
## 10:      0.59375         -0.022 36     0.64 :(
## 11:      0.65625         -0.017 31     0.52 :(
## 12:      0.71875         -0.150 33   0.0019 **
## 13:      0.78125         -0.140 32    0.005 **
## 14:      0.84375         -0.260 19    0.003 **
## 15:      0.90625         -0.190 15 0.00071 ***
## 16:      0.96875         -0.150 10   0.0053 **
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 22     0.21 :(
##  2: 25     0.68 :(
##  3: 37     0.066 .
##  4: 36     0.057 .
##  5: 34     0.38 :(
##  6: 35     0.39 :(
##  7: 32     0.86 :(
##  8: 35     0.53 :(
##  9: 35     0.23 :(
## 10: 36     0.64 :(
## 11: 31     0.52 :(
## 12: 33   0.0019 **
## 13: 32    0.005 **
## 14: 19    0.003 **
## 15: 15 0.00071 ***
## 16: 10   0.0053 **
## [1] 29.2
## [1] 8.38

## [1] "medium"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         -0.031 29     0.44 :(
##  2:      0.09375          0.051 37     0.055 .
##  3:      0.15625         -0.037 41     0.17 :(
##  4:      0.21875          0.013 43     0.93 :(
##  5:      0.28125         -0.031 45     0.65 :(
##  6:      0.34375         -0.058 39     0.13 :(
##  7:      0.40625         -0.097 45     0.069 .
##  8:      0.46875         -0.026 43     0.67 :(
##  9:      0.53125         -0.091 40      0.1 :(
## 10:      0.59375         -0.110 40     0.038 *
## 11:      0.65625         -0.022 45     0.42 :(
## 12:      0.71875         -0.094 43   0.0089 **
## 13:      0.78125         -0.130 43 0.00019 ***
## 14:      0.84375         -0.200 43 1.7e-06 ***
## 15:      0.90625         -0.210 34 3.7e-07 ***
## 16:      0.96875         -0.180 32 7.9e-07 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 29     0.44 :(
##  2: 37     0.055 .
##  3: 41     0.17 :(
##  4: 43     0.93 :(
##  5: 45     0.65 :(
##  6: 39     0.13 :(
##  7: 45     0.069 .
##  8: 43     0.67 :(
##  9: 40      0.1 :(
## 10: 40     0.038 *
## 11: 45     0.42 :(
## 12: 43   0.0089 **
## 13: 43 0.00019 ***
## 14: 43 1.7e-06 ***
## 15: 34 3.7e-07 ***
## 16: 32 7.9e-07 ***
## [1] 40.1
## [1] 4.84

## [1] "bad"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  6          NA
##  2:      0.09375        -0.0044 15     0.88 :(
##  3:      0.15625         0.0290 22     0.15 :(
##  4:      0.21875         0.0670 23     0.17 :(
##  5:      0.28125         0.1400 24     0.065 .
##  6:      0.34375         0.0850 24     0.22 :(
##  7:      0.40625        -0.0045 25     0.91 :(
##  8:      0.46875        -0.0830 24     0.19 :(
##  9:      0.53125        -0.1000 21     0.14 :(
## 10:      0.59375        -0.0730 27      0.2 :(
## 11:      0.65625        -0.0670 24     0.23 :(
## 12:      0.71875        -0.1500 27   0.0059 **
## 13:      0.78125        -0.1300 28   0.0013 **
## 14:      0.84375        -0.1900 29 1.2e-05 ***
## 15:      0.90625        -0.1600 28 3.9e-06 ***
## 16:      0.96875        -0.2200 28   4e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 15     0.88 :(
##  2: 22     0.15 :(
##  3: 23     0.17 :(
##  4: 24     0.065 .
##  5: 24     0.22 :(
##  6: 25     0.91 :(
##  7: 24     0.19 :(
##  8: 21     0.14 :(
##  9: 27      0.2 :(
## 10: 24     0.23 :(
## 11: 27   0.0059 **
## 12: 28   0.0013 **
## 13: 29 1.2e-05 ***
## 14: 28 3.9e-06 ***
## 15: 28   4e-06 ***
## [1] 24.6
## [1] 3.6

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing missing values (geom_errorbar).

Motor task

## [1] "all"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375        -0.0940  8   0.71 :(
##  3:      0.15625        -0.0990 29   0.021 *
##  4:      0.21875        -0.0760 41   0.042 *
##  5:      0.28125        -0.0540 48    0.2 :(
##  6:      0.34375        -0.0400 50   0.22 :(
##  7:      0.40625        -0.0015 50    0.9 :(
##  8:      0.46875        -0.0022 54      1 :(
##  9:      0.53125         0.0400 52   0.17 :(
## 10:      0.59375         0.0063 51   0.82 :(
## 11:      0.65625         0.0220 52   0.79 :(
## 12:      0.71875        -0.0580 53   0.064 .
## 13:      0.78125        -0.0790 46   0.015 *
## 14:      0.84375        -0.0940 29   0.077 .
## 15:      0.90625        -0.0760 13 0.0012 **
## 16:      0.96875        -0.1100  6   0.031 *
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  8   0.71 :(
##  2: 29   0.021 *
##  3: 41   0.042 *
##  4: 48    0.2 :(
##  5: 50   0.22 :(
##  6: 50    0.9 :(
##  7: 54      1 :(
##  8: 52   0.17 :(
##  9: 51   0.82 :(
## 10: 52   0.79 :(
## 11: 53   0.064 .
## 12: 46   0.015 *
## 13: 29   0.077 .
## 14: 13 0.0012 **
## 15:  6   0.031 *
## [1] 38.8
## [1] 17.3

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "good"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125             NA  0      NA
##  2:      0.09375         -0.094  8 0.71 :(
##  3:      0.15625         -0.099 24 0.023 *
##  4:      0.21875         -0.066 25 0.067 .
##  5:      0.28125         -0.043 25 0.31 :(
##  6:      0.34375         -0.040 25 0.32 :(
##  7:      0.40625          0.040 24  0.4 :(
##  8:      0.46875          0.067 24 0.12 :(
##  9:      0.53125          0.110 23 0.021 *
## 10:      0.59375          0.120 22 0.043 *
## 11:      0.65625          0.029 22 0.52 :(
## 12:      0.71875         -0.040 21 0.094 .
## 13:      0.78125         -0.067 15 0.32 :(
## 14:      0.84375             NA  2      NA
## 15:      0.90625             NA  0      NA
## 16:      0.96875             NA  0      NA
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1:  8 0.71 :(
##  2: 24 0.023 *
##  3: 25 0.067 .
##  4: 25 0.31 :(
##  5: 25 0.32 :(
##  6: 24  0.4 :(
##  7: 24 0.12 :(
##  8: 23 0.021 *
##  9: 22 0.043 *
## 10: 22 0.52 :(
## 11: 21 0.094 .
## 12: 15 0.32 :(
## [1] 21.5
## [1] 5.07

## Warning: Removed 4 rows containing missing values (geom_point).

## Warning: Removed 4 rows containing missing values (geom_errorbar).

## [1] "medium"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125             NA  0      NA
##  2:      0.09375             NA  0      NA
##  3:      0.15625             NA  5      NA
##  4:      0.21875        -0.0045 16 0.41 :(
##  5:      0.28125        -0.0670 23 0.51 :(
##  6:      0.34375        -0.0580 24  0.3 :(
##  7:      0.40625        -0.0320 25 0.73 :(
##  8:      0.46875        -0.0400 25  0.5 :(
##  9:      0.53125         0.0220 25 0.69 :(
## 10:      0.59375        -0.0220 22  0.9 :(
## 11:      0.65625         0.0410 23 0.66 :(
## 12:      0.71875         0.0310 25 0.65 :(
## 13:      0.78125        -0.0670 25 0.13 :(
## 14:      0.84375        -0.0940 20 0.15 :(
## 15:      0.90625             NA  6      NA
## 16:      0.96875             NA  0      NA
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1: 16 0.41 :(
##  2: 23 0.51 :(
##  3: 24  0.3 :(
##  4: 25 0.73 :(
##  5: 25  0.5 :(
##  6: 25 0.69 :(
##  7: 22  0.9 :(
##  8: 23 0.66 :(
##  9: 25 0.65 :(
## 10: 25 0.13 :(
## 11: 20 0.15 :(
## [1] 23
## [1] 2.83

## Warning: Removed 5 rows containing missing values (geom_point).

## Warning: Removed 5 rows containing missing values (geom_errorbar).

## [1] "bad"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj n    pval
##  1:      0.03125             NA 0      NA
##  2:      0.09375             NA 0      NA
##  3:      0.15625             NA 0      NA
##  4:      0.21875             NA 0      NA
##  5:      0.28125             NA 0      NA
##  6:      0.34375             NA 1      NA
##  7:      0.40625             NA 1      NA
##  8:      0.46875         -0.150 5 0.28 :(
##  9:      0.53125         -0.220 4 0.38 :(
## 10:      0.59375         -0.290 7 0.078 .
## 11:      0.65625         -0.130 7 0.35 :(
## 12:      0.71875         -0.260 7 0.047 *
## 13:      0.78125         -0.160 6 0.16 :(
## 14:      0.84375         -0.120 7  0.2 :(
## 15:      0.90625         -0.081 7 0.022 *
## 16:      0.96875         -0.110 6 0.031 *
## [1] "mean and sd of nb players per bin"
##    nb    pval
## 1:  5 0.28 :(
## 2:  4 0.38 :(
## 3:  7 0.078 .
## 4:  7 0.35 :(
## 5:  7 0.047 *
## 6:  6 0.16 :(
## 7:  7  0.2 :(
## 8:  7 0.022 *
## 9:  6 0.031 *
## [1] 6.22
## [1] 1.09

## Warning: Removed 7 rows containing missing values (geom_point).

## Warning: Removed 7 rows containing missing values (geom_errorbar).

Sensory task

## [1] "all"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125        -0.0310 32     0.034 *
##  2:      0.09375        -0.0065 48     0.64 :(
##  3:      0.15625        -0.0970 51   0.0069 **
##  4:      0.21875        -0.0760 47   0.0011 **
##  5:      0.28125        -0.0670 46      0.1 :(
##  6:      0.34375        -0.1300 41     0.063 .
##  7:      0.40625        -0.1200 44     0.053 .
##  8:      0.46875        -0.1100 42     0.036 *
##  9:      0.53125        -0.1700 34   0.0079 **
## 10:      0.59375        -0.2400 37 0.00062 ***
## 11:      0.65625        -0.1100 40     0.12 :(
## 12:      0.71875        -0.1700 46 0.00063 ***
## 13:      0.78125        -0.1700 42   0.0042 **
## 14:      0.84375        -0.1700 46   9e-06 ***
## 15:      0.90625        -0.1600 53 1.9e-10 ***
## 16:      0.96875        -0.1400 55 9.4e-11 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 32     0.034 *
##  2: 48     0.64 :(
##  3: 51   0.0069 **
##  4: 47   0.0011 **
##  5: 46      0.1 :(
##  6: 41     0.063 .
##  7: 44     0.053 .
##  8: 42     0.036 *
##  9: 34   0.0079 **
## 10: 37 0.00062 ***
## 11: 40     0.12 :(
## 12: 46 0.00063 ***
## 13: 42   0.0042 **
## 14: 46   9e-06 ***
## 15: 53 1.9e-10 ***
## 16: 55 9.4e-11 ***
## [1] 44
## [1] 6.4

## [1] "good"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125          0.054 10   0.36 :(
##  2:      0.09375             NA 10        NA
##  3:      0.15625         -0.110  9   0.18 :(
##  4:      0.21875         -0.150  5   0.099 .
##  5:      0.28125         -0.100  8   0.53 :(
##  6:      0.34375         -0.130  6    0.4 :(
##  7:      0.40625         -0.190  7   0.27 :(
##  8:      0.46875         -0.250  9    0.07 .
##  9:      0.53125         -0.210  7    0.2 :(
## 10:      0.59375         -0.380  6   0.058 .
## 11:      0.65625         -0.085  5   0.59 :(
## 12:      0.71875         -0.290  9   0.044 *
## 13:      0.78125         -0.250  7   0.15 :(
## 14:      0.84375         -0.130  7    0.2 :(
## 15:      0.90625         -0.085  9  0.008 **
## 16:      0.96875         -0.150 10 0.0053 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1: 10   0.36 :(
##  2:  9   0.18 :(
##  3:  5   0.099 .
##  4:  8   0.53 :(
##  5:  6    0.4 :(
##  6:  7   0.27 :(
##  7:  9    0.07 .
##  8:  7    0.2 :(
##  9:  6   0.058 .
## 10:  5   0.59 :(
## 11:  9   0.044 *
## 12:  7   0.15 :(
## 13:  7    0.2 :(
## 14:  9  0.008 **
## 15: 10 0.0053 **
## [1] 7.6
## [1] 1.68

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "medium"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         -0.031 19    0.001 **
##  2:      0.09375         -0.004 27     0.97 :(
##  3:      0.15625         -0.160 26 9.3e-05 ***
##  4:      0.21875         -0.150 24 0.00077 ***
##  5:      0.28125         -0.170 22      0.05 .
##  6:      0.34375         -0.170 19     0.14 :(
##  7:      0.40625         -0.190 22     0.025 *
##  8:      0.46875         -0.040 23     0.45 :(
##  9:      0.53125         -0.220 18      0.02 *
## 10:      0.59375         -0.270 20   0.0015 **
## 11:      0.65625         -0.160 22     0.079 .
## 12:      0.71875         -0.150 21   0.0059 **
## 13:      0.78125         -0.250 19     0.034 *
## 14:      0.84375         -0.220 24 0.00023 ***
## 15:      0.90625         -0.190 27 5.7e-06 ***
## 16:      0.96875         -0.150 27 5.5e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 19    0.001 **
##  2: 27     0.97 :(
##  3: 26 9.3e-05 ***
##  4: 24 0.00077 ***
##  5: 22      0.05 .
##  6: 19     0.14 :(
##  7: 22     0.025 *
##  8: 23     0.45 :(
##  9: 18      0.02 *
## 10: 20   0.0015 **
## 11: 22     0.079 .
## 12: 21   0.0059 **
## 13: 19     0.034 *
## 14: 24 0.00023 ***
## 15: 27 5.7e-06 ***
## 16: 27 5.5e-06 ***
## [1] 22.5
## [1] 3.1

## [1] "bad"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  3          NA
##  2:      0.09375         0.0650 11     0.56 :(
##  3:      0.15625         0.0360 16     0.42 :(
##  4:      0.21875        -0.0045 18     0.76 :(
##  5:      0.28125         0.0045 16      0.9 :(
##  6:      0.34375        -0.1300 16     0.58 :(
##  7:      0.40625         0.0580 15     0.51 :(
##  8:      0.46875        -0.1000 10     0.15 :(
##  9:      0.53125        -0.1000  9     0.72 :(
## 10:      0.59375        -0.1500 11     0.82 :(
## 11:      0.65625         0.0220 13     0.78 :(
## 12:      0.71875        -0.0400 16     0.36 :(
## 13:      0.78125        -0.0710 16     0.26 :(
## 14:      0.84375        -0.0820 15      0.07 .
## 15:      0.90625        -0.1600 17 0.00027 ***
## 16:      0.96875        -0.1300 18 0.00021 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 11     0.56 :(
##  2: 16     0.42 :(
##  3: 18     0.76 :(
##  4: 16      0.9 :(
##  5: 16     0.58 :(
##  6: 15     0.51 :(
##  7: 10     0.15 :(
##  8:  9     0.72 :(
##  9: 11     0.82 :(
## 10: 13     0.78 :(
## 11: 16     0.36 :(
## 12: 16     0.26 :(
## 13: 15      0.07 .
## 14: 17 0.00027 ***
## 15: 18 0.00021 ***
## [1] 14.5
## [1] 2.92

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing missing values (geom_errorbar).

Logical task

## [1] "all"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0005 38     0.78 :(
##  2:      0.09375         0.0970 43      0.01 *
##  3:      0.15625         0.0940 48      0.04 *
##  4:      0.21875         0.1600 50   0.0046 **
##  5:      0.28125         0.1500 49     0.015 *
##  6:      0.34375         0.0850 41      0.08 .
##  7:      0.40625         0.0220 47     0.77 :(
##  8:      0.46875        -0.0400 47     0.64 :(
##  9:      0.53125         0.0160 45     0.73 :(
## 10:      0.59375        -0.0370 46      0.6 :(
## 11:      0.65625        -0.0490 42     0.32 :(
## 12:      0.71875        -0.1500 41 0.00057 ***
## 13:      0.78125        -0.1400 53 0.00026 ***
## 14:      0.84375        -0.2600 52 1.4e-08 ***
## 15:      0.90625        -0.2400 42 1.7e-08 ***
## 16:      0.96875        -0.3300 29 2.7e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 38     0.78 :(
##  2: 43      0.01 *
##  3: 48      0.04 *
##  4: 50   0.0046 **
##  5: 49     0.015 *
##  6: 41      0.08 .
##  7: 47     0.77 :(
##  8: 47     0.64 :(
##  9: 45     0.73 :(
## 10: 46      0.6 :(
## 11: 42     0.32 :(
## 12: 41 0.00057 ***
## 13: 53 0.00026 ***
## 14: 52 1.4e-08 ***
## 15: 42 1.7e-08 ***
## 16: 29 2.7e-06 ***
## [1] 44.6
## [1] 5.93

## [1] "good"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125          0.012 16 0.51 :(
##  2:      0.09375          0.044 16 0.37 :(
##  3:      0.15625          0.058 15  0.8 :(
##  4:      0.21875          0.031 14  0.8 :(
##  5:      0.28125         -0.048 11 0.69 :(
##  6:      0.34375         -0.082 13 0.67 :(
##  7:      0.40625         -0.085  9 0.72 :(
##  8:      0.46875         -0.040 14 0.49 :(
##  9:      0.53125          0.040 14 0.45 :(
## 10:      0.59375         -0.170 13 0.18 :(
## 11:      0.65625         -0.160 11 0.12 :(
## 12:      0.71875         -0.280 11 0.014 *
## 13:      0.78125         -0.280 15 0.033 *
## 14:      0.84375         -0.390 12 0.011 *
## 15:      0.90625         -0.410  7 0.022 *
## 16:      0.96875             NA  0      NA
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1: 16 0.51 :(
##  2: 16 0.37 :(
##  3: 15  0.8 :(
##  4: 14  0.8 :(
##  5: 11 0.69 :(
##  6: 13 0.67 :(
##  7:  9 0.72 :(
##  8: 14 0.49 :(
##  9: 14 0.45 :(
## 10: 13 0.18 :(
## 11: 11 0.12 :(
## 12: 11 0.014 *
## 13: 15 0.033 *
## 14: 12 0.011 *
## 15:  7 0.022 *
## [1] 12.7
## [1] 2.58

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "medium"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.016 18     0.75 :(
##  2:      0.09375          0.150 21   0.0031 **
##  3:      0.15625          0.130 22     0.048 *
##  4:      0.21875          0.190 21     0.016 *
##  5:      0.28125          0.110 21     0.13 :(
##  6:      0.34375          0.085 14     0.23 :(
##  7:      0.40625          0.022 20     0.42 :(
##  8:      0.46875          0.140 17     0.32 :(
##  9:      0.53125          0.040 16     0.78 :(
## 10:      0.59375         -0.022 17     0.96 :(
## 11:      0.65625         -0.013 18     0.79 :(
## 12:      0.71875         -0.150 17      0.2 :(
## 13:      0.78125         -0.140 20     0.12 :(
## 14:      0.84375         -0.200 21 0.00015 ***
## 15:      0.90625         -0.200 16 0.00048 ***
## 16:      0.96875         -0.400 10   0.0057 **
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 18     0.75 :(
##  2: 21   0.0031 **
##  3: 22     0.048 *
##  4: 21     0.016 *
##  5: 21     0.13 :(
##  6: 14     0.23 :(
##  7: 20     0.42 :(
##  8: 17     0.32 :(
##  9: 16     0.78 :(
## 10: 17     0.96 :(
## 11: 18     0.79 :(
## 12: 17      0.2 :(
## 13: 20     0.12 :(
## 14: 21 0.00015 ***
## 15: 16 0.00048 ***
## 16: 10   0.0057 **
## [1] 18.1
## [1] 3.17

## [1] "bad"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  4          NA
##  2:      0.09375             NA  6          NA
##  3:      0.15625          0.082 11     0.26 :(
##  4:      0.21875          0.210 15     0.056 .
##  5:      0.28125          0.220 17     0.017 *
##  6:      0.34375          0.230 14     0.032 *
##  7:      0.40625          0.022 18     0.97 :(
##  8:      0.46875         -0.040 16     0.34 :(
##  9:      0.53125         -0.031 15     0.93 :(
## 10:      0.59375          0.085 16     0.66 :(
## 11:      0.65625          0.022 13     0.83 :(
## 12:      0.71875         -0.150 13     0.025 *
## 13:      0.78125         -0.120 18     0.011 *
## 14:      0.84375         -0.220 19   0.0013 **
## 15:      0.90625         -0.190 19 0.00014 ***
## 16:      0.96875         -0.300 19 0.00014 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 11     0.26 :(
##  2: 15     0.056 .
##  3: 17     0.017 *
##  4: 14     0.032 *
##  5: 18     0.97 :(
##  6: 16     0.34 :(
##  7: 15     0.93 :(
##  8: 16     0.66 :(
##  9: 13     0.83 :(
## 10: 13     0.025 *
## 11: 18     0.011 *
## 12: 19   0.0013 **
## 13: 19 0.00014 ***
## 14: 19 0.00014 ***
## [1] 15.9
## [1] 2.56

## Warning: Removed 2 rows containing missing values (geom_point).

## Warning: Removed 2 rows containing missing values (geom_errorbar).

Influence of Playtime on Subjective Difficulty Error

For all groups, motor, sensitive and logical

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.85425  -0.20543   0.02783   0.20243   0.70750  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.006664   0.018685  -0.357 0.721400    
## timeNorm     0.016339   0.020930   0.781 0.435103    
## obj.diff    -0.094710   0.028659  -3.305 0.000969 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07620175)
## 
##     Null deviance: 143.99  on 1880  degrees of freedom
## Residual deviance: 143.11  on 1878  degrees of freedom
## AIC: 500.65
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.78155  -0.13780  -0.01151   0.12280   0.83005  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.006795   0.014205  -0.478    0.632    
## timeNorm     0.011968   0.020729   0.577    0.564    
## obj.diff    -0.205459   0.018132 -11.331   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.0706054)
## 
##     Null deviance: 137.08  on 1814  degrees of freedom
## Residual deviance: 127.94  on 1812  degrees of freedom
## AIC: 344.82
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.71153  -0.24157   0.00719   0.24129   0.65825  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.17431    0.01800   9.684   <2e-16 ***
## timeNorm     0.02107    0.02417   0.872    0.384    
## obj.diff    -0.46661    0.02329 -20.037   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1007759)
## 
##     Null deviance: 230.47  on 1880  degrees of freedom
## Residual deviance: 189.26  on 1878  degrees of freedom
## AIC: 1026.4
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n      pval
##  1:      1.5      0.3918129     0.4457102 -0.05543814 342 0.0017 **
##  2:      4.5      0.4954052     0.5624859 -0.05783222 171 0.0052 **
##  3:      7.5      0.4928989     0.5357049 -0.03693287 171   0.077 .
##  4:     10.5      0.5071011     0.5362058 -0.02578583 171   0.23 :(
##  5:     13.5      0.4519632     0.5133937 -0.05576376 171 0.0061 **
##  6:     16.5      0.4995823     0.5320036 -0.01539779 171   0.46 :(
##  7:     19.5      0.4803676     0.5358363 -0.04608428 171   0.025 *
##  8:     22.5      0.4527987     0.4961373 -0.03638516 171   0.091 .
##  9:     25.5      0.4536341     0.4868060 -0.02527202 171   0.27 :(
## 10:     28.5      0.4243943     0.4657574 -0.03934980 171   0.074 .
##     time  error.diff shapes
##  1:  1.5 -0.05543814     24
##  2:  4.5 -0.05783222     24
##  3:  7.5 -0.03693287     16
##  4: 10.5 -0.02578583     16
##  5: 13.5 -0.05576376     24
##  6: 16.5 -0.01539779     16
##  7: 19.5 -0.04608428     24
##  8: 22.5 -0.03638516     16
##  9: 25.5 -0.02527202     16
## 10: 28.5 -0.03934980     16

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.2761905     0.3121174 -0.08025653 330 2.2e-05 ***
##  2:      4.5      0.5194805     0.6623901 -0.12246157 165 6.5e-13 ***
##  3:      7.5      0.4259740     0.5756691 -0.12748326 165 6.7e-13 ***
##  4:     10.5      0.4658009     0.6169890 -0.12563962 165 5.6e-14 ***
##  5:     13.5      0.4251082     0.5882784 -0.13475627 165 4.2e-16 ***
##  6:     16.5      0.4025974     0.5480044 -0.12850300 165 1.9e-12 ***
##  7:     19.5      0.4666667     0.5706900 -0.09391859 165 2.3e-08 ***
##  8:     22.5      0.4311688     0.5568448 -0.12173493 165 1.6e-10 ***
##  9:     25.5      0.4891775     0.5635905 -0.08515151 165 7.1e-08 ***
## 10:     28.5      0.4649351     0.5525507 -0.08873994 165 1.2e-07 ***
##     time  error.diff shapes
##  1:  1.5 -0.08025653     24
##  2:  4.5 -0.12246157     24
##  3:  7.5 -0.12748326     24
##  4: 10.5 -0.12563962     24
##  5: 13.5 -0.13475627     24
##  6: 16.5 -0.12850300     24
##  7: 19.5 -0.09391859     24
##  8: 22.5 -0.12173493     24
##  9: 25.5 -0.08515151     24
## 10: 28.5 -0.08873994     24

##     time.bin subj.diff.mean obj.diff.mean    error.diff   n        pval
##  1:      1.5      0.3483709     0.3515160 -0.0257788254 342     0.26 :(
##  2:      4.5      0.5037594     0.6513076 -0.1432600132 171 4.6e-08 ***
##  3:      7.5      0.5037594     0.5682979 -0.0702473202 171   0.0057 **
##  4:     10.5      0.4970760     0.5388474 -0.0530333212 171      0.04 *
##  5:     13.5      0.4761905     0.5225795 -0.0457087630 171     0.099 .
##  6:     16.5      0.4820384     0.5042410 -0.0325739632 171     0.21 :(
##  7:     19.5      0.4185464     0.4415088 -0.0319575055 171     0.25 :(
##  8:     22.5      0.3918129     0.4078173 -0.0213488721 171     0.43 :(
##  9:     25.5      0.3851295     0.3856125 -0.0035008941 171      0.9 :(
## 10:     28.5      0.3792815     0.3513216 -0.0006985616 171     0.98 :(
##     time    error.diff shapes
##  1:  1.5 -0.0257788254     16
##  2:  4.5 -0.1432600132     24
##  3:  7.5 -0.0702473202     24
##  4: 10.5 -0.0530333212     24
##  5: 13.5 -0.0457087630     16
##  6: 16.5 -0.0325739632     16
##  7: 19.5 -0.0319575055     16
##  8: 22.5 -0.0213488721     16
##  9: 25.5 -0.0035008941     16
## 10: 28.5 -0.0006985616     16

For all taks, per group

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7513  -0.2546   0.1076   0.1867   0.6336  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.179774   0.023379   7.690 2.71e-14 ***
## timeNorm     0.004733   0.027013   0.175    0.861    
## obj.diff    -0.424140   0.027985 -15.156  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.09792818)
## 
##     Null deviance: 164.4  on 1451  degrees of freedom
## Residual deviance: 141.9  on 1449  degrees of freedom
## AIC: 751.84
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.78017  -0.19691   0.00829   0.20665   0.75977  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.04695    0.01439   3.263  0.00112 ** 
## timeNorm     0.05092    0.01926   2.643  0.00827 ** 
## obj.diff    -0.27040    0.02001 -13.517  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.08353945)
## 
##     Null deviance: 219.31  on 2441  degrees of freedom
## Residual deviance: 203.75  on 2439  degrees of freedom
## AIC: 872.98
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.73363  -0.19029  -0.06711   0.20028   0.79071  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.04261    0.01592   2.676  0.00752 ** 
## timeNorm     0.01363    0.02197   0.620  0.53521    
## obj.diff    -0.22419    0.02565  -8.741  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07507909)
## 
##     Null deviance: 131.93  on 1682  degrees of freedom
## Residual deviance: 126.13  on 1680  degrees of freedom
## AIC: 423.5
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.4610390     0.4913488 -0.03726018 264     0.11 :(
##  2:      4.5      0.6341991     0.7895046 -0.12291013 132 2.5e-09 ***
##  3:      7.5      0.6103896     0.7343967 -0.11292250 132 2.7e-06 ***
##  4:     10.5      0.6060606     0.7058964 -0.09439936 132 0.00028 ***
##  5:     13.5      0.5725108     0.6981354 -0.11604262 132 5.5e-07 ***
##  6:     16.5      0.5790043     0.6912326 -0.09793743 132   8e-05 ***
##  7:     19.5      0.5454545     0.6352225 -0.08199724 132   0.0046 **
##  8:     22.5      0.5422078     0.6448623 -0.09093136 132 0.00045 ***
##  9:     25.5      0.5162338     0.6145444 -0.09204714 132   1e-04 ***
## 10:     28.5      0.5551948     0.5903874 -0.04478824 132      0.03 *
##     time  error.diff shapes
##  1:  1.5 -0.03726018     16
##  2:  4.5 -0.12291013     24
##  3:  7.5 -0.11292250     24
##  4: 10.5 -0.09439936     24
##  5: 13.5 -0.11604262     24
##  6: 16.5 -0.09793743     24
##  7: 19.5 -0.08199724     24
##  8: 22.5 -0.09093136     24
##  9: 25.5 -0.09204714     24
## 10: 28.5 -0.04478824     24

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.3156371     0.3551579 -0.06288359 444 0.00064 ***
##  2:      4.5      0.4890605     0.6270722 -0.12982738 222 1.5e-11 ***
##  3:      7.5      0.4523810     0.5268408 -0.07581702 222 2.7e-05 ***
##  4:     10.5      0.4890605     0.5721783 -0.08412021 222 3.5e-06 ***
##  5:     13.5      0.4613900     0.5432406 -0.07663430 222 0.00011 ***
##  6:     16.5      0.4536680     0.5171087 -0.06901275 222 0.00093 ***
##  7:     19.5      0.4877735     0.5431912 -0.05366792 222   0.0028 **
##  8:     22.5      0.4002574     0.4701726 -0.07762486 222   1e-04 ***
##  9:     25.5      0.4633205     0.4803948 -0.02789451 222     0.21 :(
## 10:     28.5      0.4453024     0.4790999 -0.04511493 222      0.03 *
##     time  error.diff shapes
##  1:  1.5 -0.06288359     24
##  2:  4.5 -0.12982738     24
##  3:  7.5 -0.07581702     24
##  4: 10.5 -0.08412021     24
##  5: 13.5 -0.07663430     24
##  6: 16.5 -0.06901275     24
##  7: 19.5 -0.05366792     24
##  8: 22.5 -0.07762486     24
##  9: 25.5 -0.02789451     16
## 10: 28.5 -0.04511493     24

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n      pval
##  1:      1.5      0.2693744     0.2883788 -0.04855496 306 0.0056 **
##  2:      4.5      0.4201681     0.4799245 -0.06041464 153   0.018 *
##  3:      7.5      0.3902894     0.4566723 -0.06315644 153 0.0081 **
##  4:     10.5      0.3921569     0.4276822 -0.04194343 153   0.069 .
##  5:     13.5      0.3323996     0.4017259 -0.07554251 153 0.0011 **
##  6:     16.5      0.3734827     0.4024687 -0.03181379 153   0.16 :(
##  7:     19.5      0.3295985     0.3715820 -0.04710171 153   0.026 *
##  8:     22.5      0.3604108     0.3722581 -0.02073660 153   0.39 :(
##  9:     25.5      0.3473389     0.3556109 -0.01581682 153   0.52 :(
## 10:     28.5      0.2745098     0.3045755 -0.04731910 153   0.025 *
##     time  error.diff shapes
##  1:  1.5 -0.04855496     24
##  2:  4.5 -0.06041464     24
##  3:  7.5 -0.06315644     24
##  4: 10.5 -0.04194343     16
##  5: 13.5 -0.07554251     24
##  6: 16.5 -0.03181379     16
##  7: 19.5 -0.04710171     24
##  8: 22.5 -0.02073660     16
##  9: 25.5 -0.01581682     16
## 10: 28.5 -0.04731910     24

Per group, motor task

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.78307  -0.20030   0.09965   0.20287   0.56643  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.22999    0.11949  -1.925   0.0555 .
## timeNorm    -0.03598    0.06998  -0.514   0.6077  
## obj.diff     0.08283    0.15050   0.550   0.5826  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1038657)
## 
##     Null deviance: 23.736  on 230  degrees of freedom
## Residual deviance: 23.681  on 228  degrees of freedom
## AIC: 137.39
## 
## Number of Fisher Scoring iterations: 2

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): cannot compute exact confidence interval with ties

##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.5374150     0.7170177 -0.17337940 42     0.011 *
##  2:      4.5      0.6122449     0.7946114 -0.14180958 21   0.0063 **
##  3:      7.5      0.6326531     0.7649789 -0.07966557 21     0.038 *
##  4:     10.5      0.6394558     0.7869246 -0.09982316 21    0.009 **
##  5:     13.5      0.6190476     0.8120284 -0.09702938 21     0.013 *
##  6:     16.5      0.5102041     0.7887369 -0.26023913 21   0.0049 **
##  7:     19.5      0.5442177     0.7250289 -0.16961596 21      0.05 .
##  8:     22.5      0.6462585     0.7637626 -0.03896849 21     0.49 :(
##  9:     25.5      0.5578231     0.8157609 -0.26561302 21 0.00072 ***
## 10:     28.5      0.5986395     0.7674702 -0.09317669 21      0.06 .
##     time  error.diff shapes
##  1:  1.5 -0.17337940     24
##  2:  4.5 -0.14180958     24
##  3:  7.5 -0.07966557     24
##  4: 10.5 -0.09982316     24
##  5: 13.5 -0.09702938     24
##  6: 16.5 -0.26023913     24
##  7: 19.5 -0.16961596     16
##  8: 22.5 -0.03896849     16
##  9: 25.5 -0.26561302     24
## 10: 28.5 -0.09317669     16

## Warning: Removed 2 rows containing missing values (geom_errorbar).

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.79768  -0.22927   0.04496   0.19205   0.68904  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04646    0.03146  -1.477    0.140
## timeNorm     0.01656    0.03145   0.527    0.599
## obj.diff    -0.01154    0.04892  -0.236    0.814
## 
## (Dispersion parameter for gaussian family taken to be 0.07542452)
## 
##     Null deviance: 62.024  on 824  degrees of freedom
## Residual deviance: 61.999  on 822  degrees of freedom
## AIC: 213.93
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n    pval
##  1:      1.5      0.4219048     0.4703926 -0.04882302 150 0.077 .
##  2:      4.5      0.5314286     0.6181213 -0.07545117  75  0.02 *
##  3:      7.5      0.5028571     0.5405554 -0.02915190  75 0.35 :(
##  4:     10.5      0.5333333     0.5682867 -0.03243828  75 0.34 :(
##  5:     13.5      0.5200000     0.5516441 -0.02674953  75 0.43 :(
##  6:     16.5      0.5428571     0.5685882 -0.02122707  75 0.62 :(
##  7:     19.5      0.5447619     0.5794923 -0.02965771  75 0.39 :(
##  8:     22.5      0.4380952     0.5231952 -0.09195474  75 0.015 *
##  9:     25.5      0.4819048     0.5079792 -0.03043348  75 0.41 :(
## 10:     28.5      0.4819048     0.5148979 -0.03878182  75 0.31 :(
##     time  error.diff shapes
##  1:  1.5 -0.04882302     16
##  2:  4.5 -0.07545117     24
##  3:  7.5 -0.02915190     16
##  4: 10.5 -0.03243828     16
##  5: 13.5 -0.02674953     16
##  6: 16.5 -0.02122707     16
##  7: 19.5 -0.02965771     16
##  8: 22.5 -0.09195474     24
##  9: 25.5 -0.03043348     16
## 10: 28.5 -0.03878182     16

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.80335  -0.18155  -0.01888   0.19399   0.72918  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.06141    0.02493  -2.463   0.0140 *
## timeNorm     0.03220    0.02896   1.112   0.2664  
## obj.diff     0.08952    0.04587   1.952   0.0513 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06387276)
## 
##     Null deviance: 52.815  on 824  degrees of freedom
## Residual deviance: 52.503  on 822  degrees of freedom
## AIC: 76.782
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n    pval
##  1:      1.5      0.3209524     0.3450617 -0.03697026 150 0.13 :(
##  2:      4.5      0.4266667     0.4418554 -0.01357897  75 0.61 :(
##  3:      7.5      0.4438095     0.4666577 -0.02092597  75 0.44 :(
##  4:     10.5      0.4438095     0.4339237  0.01170254  75 0.74 :(
##  5:     13.5      0.3371429     0.3915256 -0.05898709  75 0.041 *
##  6:     16.5      0.4533333     0.4235337  0.02549517  75 0.27 :(
##  7:     19.5      0.3980952     0.4392064 -0.03752952  75 0.23 :(
##  8:     22.5      0.4133333     0.3941442  0.01243771  75 0.56 :(
##  9:     25.5      0.3961905     0.3735255  0.02575106  75 0.45 :(
## 10:     28.5      0.3180952     0.3321373 -0.01719639  75 0.56 :(
##     time  error.diff shapes
##  1:  1.5 -0.03697026     16
##  2:  4.5 -0.01357897     16
##  3:  7.5 -0.02092597     16
##  4: 10.5  0.01170254     16
##  5: 13.5 -0.05898709     24
##  6: 16.5  0.02549517     16
##  7: 19.5 -0.03752952     16
##  8: 22.5  0.01243771     16
##  9: 25.5  0.02575106     16
## 10: 28.5 -0.01719639     16

Per group, sensory task

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.80489  -0.19715   0.04686   0.12205   0.71041  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.10731    0.02825   3.799 0.000161 ***
## timeNorm    -0.01236    0.03779  -0.327 0.743602    
## obj.diff    -0.28137    0.03440  -8.179 1.76e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07744331)
## 
##     Null deviance: 51.071  on 593  degrees of freedom
## Residual deviance: 45.769  on 591  degrees of freedom
## AIC: 171.12
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.4060847     0.3647578  0.01440955 108     0.66 :(
##  2:      4.5      0.6481481     0.7356615 -0.10467396  54   0.0024 **
##  3:      7.5      0.5925926     0.7147027 -0.11933066  54   0.0012 **
##  4:     10.5      0.5767196     0.7227008 -0.12067509  54 0.00057 ***
##  5:     13.5      0.5343915     0.6812848 -0.12776766  54 4.4e-06 ***
##  6:     16.5      0.5291005     0.6163897 -0.09974710  54     0.011 *
##  7:     19.5      0.5449735     0.6057373 -0.09050373  54      0.1 :(
##  8:     22.5      0.5449735     0.6322030 -0.11182543  54   0.0041 **
##  9:     25.5      0.5000000     0.5850422 -0.09763863  54 0.00011 ***
## 10:     28.5      0.5846561     0.5894469 -0.04923471  54     0.17 :(
##     time  error.diff shapes
##  1:  1.5  0.01440955     16
##  2:  4.5 -0.10467396     24
##  3:  7.5 -0.11933066     24
##  4: 10.5 -0.12067509     24
##  5: 13.5 -0.12776766     24
##  6: 16.5 -0.09974710     24
##  7: 19.5 -0.09050373     16
##  8: 22.5 -0.11182543     24
##  9: 25.5 -0.09763863     24
## 10: 28.5 -0.04923471     16

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7546  -0.1203  -0.0149   0.1332   0.8520  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.04967    0.01924  -2.582  0.00998 ** 
## timeNorm     0.02696    0.02862   0.942  0.34649    
## obj.diff    -0.19284    0.02517  -7.662 4.78e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06551003)
## 
##     Null deviance: 62.028  on 890  degrees of freedom
## Residual deviance: 58.173  on 888  degrees of freedom
## AIC: 105.08
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.2292769     0.3038003 -0.10333005 162 1.7e-05 ***
##  2:      4.5      0.4550265     0.6435586 -0.15826125  81 1.4e-09 ***
##  3:      7.5      0.3580247     0.5229068 -0.14268518  81 1.3e-07 ***
##  4:     10.5      0.4373898     0.6031879 -0.13652264  81   8e-10 ***
##  5:     13.5      0.3932981     0.5733030 -0.15553550  81   3e-09 ***
##  6:     16.5      0.3544974     0.5345376 -0.15396096  81 1.5e-09 ***
##  7:     19.5      0.4744268     0.5914733 -0.09274510  81 7.1e-06 ***
##  8:     22.5      0.3615520     0.5257793 -0.14843431  81 6.3e-07 ***
##  9:     25.5      0.5061728     0.5797558 -0.08411956  81   7e-04 ***
## 10:     28.5      0.4320988     0.5619786 -0.10644662  81 4.3e-07 ***
##     time  error.diff shapes
##  1:  1.5 -0.10333005     24
##  2:  4.5 -0.15826125     24
##  3:  7.5 -0.14268518     24
##  4: 10.5 -0.13652264     24
##  5: 13.5 -0.15553550     24
##  6: 16.5 -0.15396096     24
##  7: 19.5 -0.09274510     24
##  8: 22.5 -0.14843431     24
##  9: 25.5 -0.08411956     24
## 10: 28.5 -0.10644662     24

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.6201  -0.1032  -0.0058   0.1318   0.8614  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.03880    0.02941  -1.319    0.188    
## timeNorm     0.02457    0.04594   0.535    0.593    
## obj.diff    -0.20012    0.03979  -5.030 8.11e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06312685)
## 
##     Null deviance: 22.242  on 329  degrees of freedom
## Residual deviance: 20.642  on 327  degrees of freedom
## AIC: 29.825
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.1690476     0.2398207 -0.07330477 60 5.5e-06 ***
##  2:      4.5      0.4619048     0.5813467 -0.09880129 30    0.004 **
##  3:      7.5      0.3095238     0.4678668 -0.11016732 30 1.2e-05 ***
##  4:     10.5      0.3428571     0.4639707 -0.10959939 30   0.0024 **
##  5:     13.5      0.3142857     0.4613004 -0.11754338 30 0.00046 ***
##  6:     16.5      0.3047619     0.4612711 -0.11813934 30 0.00095 ***
##  7:     19.5      0.3047619     0.4514902 -0.10794597 30 0.00021 ***
##  8:     22.5      0.4142857     0.5050770 -0.08925231 30    0.004 **
##  9:     25.5      0.4238095     0.4813309 -0.06206745 30     0.043 *
## 10:     28.5      0.3380952     0.4606821 -0.10198710 30   0.0081 **
##     time  error.diff shapes
##  1:  1.5 -0.07330477     24
##  2:  4.5 -0.09880129     24
##  3:  7.5 -0.11016732     24
##  4: 10.5 -0.10959939     24
##  5: 13.5 -0.11754338     24
##  6: 16.5 -0.11813934     24
##  7: 19.5 -0.10794597     24
##  8: 22.5 -0.08925231     24
##  9: 25.5 -0.06206745     24
## 10: 28.5 -0.10198710     24

Per group, logical task

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.6899  -0.3042   0.1314   0.2349   0.4789  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.327848   0.039949   8.207  1.3e-15 ***
## timeNorm    -0.004351   0.043288  -0.101     0.92    
## obj.diff    -0.633947   0.046829 -13.537  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1078885)
## 
##     Null deviance: 87.210  on 626  degrees of freedom
## Residual deviance: 67.322  on 624  degrees of freedom
## AIC: 388.23
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.4849624     0.5281359 -0.04157421 114     0.27 :(
##  2:      4.5      0.6290727     0.8386323 -0.19425581  57 1.2e-05 ***
##  3:      7.5      0.6190476     0.7417870 -0.11473387  57     0.011 *
##  4:     10.5      0.6215539     0.6601240 -0.04393235  57      0.4 :(
##  5:     13.5      0.5914787     0.6721386 -0.09528694  57      0.08 .
##  6:     16.5      0.6516291     0.7262138 -0.06587885  57     0.074 .
##  7:     19.5      0.5463659     0.6300694 -0.07167203  57     0.18 :(
##  8:     22.5      0.5012531     0.6130500 -0.08720494  57      0.04 *
##  9:     25.5      0.5162907     0.5683615 -0.05218842  57     0.43 :(
## 10:     28.5      0.5112782     0.5260374 -0.02478075  57     0.47 :(
##     time  error.diff shapes
##  1:  1.5 -0.04157421     16
##  2:  4.5 -0.19425581     24
##  3:  7.5 -0.11473387     24
##  4: 10.5 -0.04393235     16
##  5: 13.5 -0.09528694     16
##  6: 16.5 -0.06587885     16
##  7: 19.5 -0.07167203     16
##  8: 22.5 -0.08720494     24
##  9: 25.5 -0.05218842     16
## 10: 28.5 -0.02478075     16

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.70616  -0.25599   0.03072   0.24287   0.63940  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.16277    0.02810   5.793 1.03e-08 ***
## timeNorm     0.08214    0.03854   2.132   0.0334 *  
## obj.diff    -0.46349    0.03912 -11.849  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.09911969)
## 
##     Null deviance: 86.496  on 725  degrees of freedom
## Residual deviance: 71.664  on 723  degrees of freedom
## AIC: 387.2
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n     pval
##  1:      1.5      0.3008658     0.2872390 -0.008624106 132  0.87 :(
##  2:      4.5      0.4826840     0.6170103 -0.137094482  66 0.001 **
##  3:      7.5      0.5108225     0.5160840 -0.023124603  66  0.57 :(
##  4:     10.5      0.5021645     0.5385434 -0.046324082  66  0.32 :(
##  5:     13.5      0.4783550     0.4967963 -0.003628038  66  0.93 :(
##  6:     16.5      0.4740260     0.4372192  0.024297695  66  0.57 :(
##  7:     19.5      0.4393939     0.4426848 -0.025355834  66  0.65 :(
##  8:     22.5      0.4047619     0.3416750  0.045008506  66  0.24 :(
##  9:     25.5      0.3896104     0.3271059  0.067289348  66  0.15 :(
## 10:     28.5      0.4199134     0.3367055  0.068892304  66  0.12 :(
##     time   error.diff shapes
##  1:  1.5 -0.008624106     16
##  2:  4.5 -0.137094482     24
##  3:  7.5 -0.023124603     16
##  4: 10.5 -0.046324082     16
##  5: 13.5 -0.003628038     16
##  6: 16.5  0.024297695     16
##  7: 19.5 -0.025355834     16
##  8: 22.5  0.045008506     16
##  9: 25.5  0.067289348     16
## 10: 28.5  0.068892304     16

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.5917  -0.1870  -0.1249   0.2308   0.7379  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.15007    0.02964   5.063 5.72e-07 ***
## timeNorm    -0.05160    0.04248  -1.215    0.225    
## obj.diff    -0.49282    0.04761 -10.351  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.08574876)
## 
##     Null deviance: 54.225  on 527  degrees of freedom
## Residual deviance: 45.018  on 525  degrees of freedom
## AIC: 206.45
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean  error.diff  n    pval
##  1:      1.5      0.2514881     0.2301605 -0.04128074 96 0.29 :(
##  2:      4.5      0.3839286     0.4760184 -0.09387526 48 0.084 .
##  3:      7.5      0.3571429     0.4340736 -0.07703553 48 0.12 :(
##  4:     10.5      0.3422619     0.3952495 -0.06600414 48  0.05 .
##  5:     13.5      0.3363095     0.3804299 -0.04404735 48 0.38 :(
##  6:     16.5      0.2916667     0.3328032 -0.05676110 48 0.24 :(
##  7:     19.5      0.2380952     0.2159761 -0.01912010 48  0.8 :(
##  8:     22.5      0.2440476     0.2550491 -0.02616089 48 0.62 :(
##  9:     25.5      0.2232143     0.2490444 -0.04238278 48 0.27 :(
## 10:     28.5      0.1666667     0.1639436 -0.03508540 48 0.12 :(
##     time  error.diff shapes
##  1:  1.5 -0.04128074     16
##  2:  4.5 -0.09387526     16
##  3:  7.5 -0.07703553     16
##  4: 10.5 -0.06600414     16
##  5: 13.5 -0.04404735     16
##  6: 16.5 -0.05676110     16
##  7: 19.5 -0.01912010     16
##  8: 22.5 -0.02616089     16
##  9: 25.5 -0.04238278     16
## 10: 28.5 -0.03508540     16

Link between d(t-1) and error

## 
## Call:
## glm(formula = error.subj.diff.mise ~ est.confidence.norm, data = DTM)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.88650  -0.19474   0.02121   0.20015   0.73863  
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)  
## (Intercept)         -0.02572    0.01258  -2.044   0.0411 *
## est.confidence.norm -0.04321    0.02231  -1.937   0.0529 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07647613)
## 
##     Null deviance: 143.99  on 1880  degrees of freedom
## Residual deviance: 143.70  on 1879  degrees of freedom
## AIC: 506.41
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ est.confidence.norm, data = DTS)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.87482  -0.09574   0.00360   0.07584   0.92532  
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         -0.10317    0.01295  -7.968 2.81e-15 ***
## est.confidence.norm -0.01433    0.02181  -0.657    0.511    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07559423)
## 
##     Null deviance: 137.08  on 1814  degrees of freedom
## Residual deviance: 137.05  on 1813  degrees of freedom
## AIC: 467.73
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ est.confidence.norm, data = DTL)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.96055  -0.18551  -0.02757   0.20982   0.87052  
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)         -0.031481   0.016416  -1.918   0.0553 .
## est.confidence.norm  0.001105   0.028651   0.039   0.9692  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1226532)
## 
##     Null deviance: 230.47  on 1880  degrees of freedom
## Residual deviance: 230.47  on 1879  degrees of freedom
## AIC: 1395
## 
## Number of Fisher Scoring iterations: 2

## Linear mixed model fit by REML t-tests use Satterthwaite approximations
##   to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
##    Data: DTAll
## 
## REML criterion at convergence: 1633.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.7925 -0.5834 -0.0572  0.5586  4.2448 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.01823  0.1350  
##  Residual             0.07577  0.2753  
## Number of obs: 5577, groups:  IDjoueur, 58
## 
## Fixed effects:
##                       Estimate Std. Error         df t value Pr(>|t|)  
## (Intercept)           -0.04362    0.01956   77.00000  -2.230   0.0286 *
## est.confidence.norm   -0.03058    0.01486 5560.00000  -2.058   0.0397 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## est.cnfdnc. -0.378

## Linear mixed model fit by REML t-tests use Satterthwaite approximations
##   to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
##    Data: DTM
## 
## REML criterion at convergence: -162
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.5725 -0.6690  0.0186  0.6541  3.3451 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.02822  0.1680  
##  Residual             0.04882  0.2209  
## Number of obs: 1881, groups:  IDjoueur, 57
## 
## Fixed effects:
##                       Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)           -0.03875    0.02664   97.80000  -1.455    0.149
## est.confidence.norm   -0.01640    0.02826 1731.70000  -0.580    0.562
## 
## Correlation of Fixed Effects:
##             (Intr)
## est.cnfdnc. -0.516

## Linear mixed model fit by REML t-tests use Satterthwaite approximations
##   to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
##    Data: DTS
## 
## REML criterion at convergence: 315.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0734 -0.4939  0.0180  0.4206  3.9205 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.01030  0.1015  
##  Residual             0.06558  0.2561  
## Number of obs: 1815, groups:  IDjoueur, 55
## 
## Fixed effects:
##                      Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)          -0.11702    0.02156 146.20000  -5.428 2.31e-07 ***
## est.confidence.norm   0.01259    0.03019 742.10000   0.417    0.677    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## est.cnfdnc. -0.721

## Linear mixed model fit by REML t-tests use Satterthwaite approximations
##   to degrees of freedom [lmerMod]
## Formula: error.subj.diff.mise ~ est.confidence.norm + (1 | IDjoueur)
##    Data: DTL
## 
## REML criterion at convergence: 998.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.5425 -0.6144 -0.0593  0.5775  3.2918 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.03255  0.1804  
##  Residual             0.09176  0.3029  
## Number of obs: 1881, groups:  IDjoueur, 57
## 
## Fixed effects:
##                       Estimate Std. Error         df t value Pr(>|t|)   
## (Intercept)           -0.08898    0.03103  118.30000  -2.867  0.00490 **
## est.confidence.norm    0.11638    0.03715 1557.40000   3.133  0.00176 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## est.cnfdnc. -0.597

CONFIDENCE SCALE APPROACH

Influence of Objective difficulty on Subjective Difficulty

All tasks

## [1] "all"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with zeroes

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with zeroes

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.150 47 1.8e-05 ***
##  2:      0.09375          0.140 54 5.2e-06 ***
##  3:      0.15625          0.140 58 1.3e-05 ***
##  4:      0.21875          0.100 58 7.1e-05 ***
##  5:      0.28125          0.110 57   1e-05 ***
##  6:      0.34375          0.090 58   0.0014 **
##  7:      0.40625          0.048 58     0.068 .
##  8:      0.46875          0.041 58     0.021 *
##  9:      0.53125         -0.031 58     0.034 *
## 10:      0.59375         -0.052 58     0.018 *
## 11:      0.65625         -0.071 58      0.01 *
## 12:      0.71875         -0.140 58 1.3e-06 ***
## 13:      0.78125         -0.170 58 6.7e-09 ***
## 14:      0.84375         -0.220 58 6.1e-09 ***
## 15:      0.90625         -0.230 57 2.1e-10 ***
## 16:      0.96875         -0.190 55 6.8e-09 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 47 1.8e-05 ***
##  2: 54 5.2e-06 ***
##  3: 58 1.3e-05 ***
##  4: 58 7.1e-05 ***
##  5: 57   1e-05 ***
##  6: 58   0.0014 **
##  7: 58     0.068 .
##  8: 58     0.021 *
##  9: 58     0.034 *
## 10: 58     0.018 *
## 11: 58      0.01 *
## 12: 58 1.3e-06 ***
## 13: 58 6.7e-09 ***
## 14: 58 6.1e-09 ***
## 15: 57 2.1e-10 ***
## 16: 55 6.8e-09 ***
## [1] 56.8
## [1] 2.86

## [1] "good"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.120 22   0.0025 **
##  2:      0.09375          0.096 25      0.1 :(
##  3:      0.15625          0.094 37     0.082 .
##  4:      0.21875          0.081 36     0.033 *
##  5:      0.28125          0.069 34     0.038 *
##  6:      0.34375          0.065 35     0.052 .
##  7:      0.40625          0.044 32     0.49 :(
##  8:      0.46875          0.031 35     0.19 :(
##  9:      0.53125          0.035 35     0.46 :(
## 10:      0.59375         -0.038 36     0.44 :(
## 11:      0.65625         -0.056 31     0.14 :(
## 12:      0.71875         -0.190 33   1e-04 ***
## 13:      0.78125         -0.180 32 0.00065 ***
## 14:      0.84375         -0.320 19 0.00033 ***
## 15:      0.90625         -0.280 15   0.0016 **
## 16:      0.96875         -0.085 10     0.18 :(
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 22   0.0025 **
##  2: 25      0.1 :(
##  3: 37     0.082 .
##  4: 36     0.033 *
##  5: 34     0.038 *
##  6: 35     0.052 .
##  7: 32     0.49 :(
##  8: 35     0.19 :(
##  9: 35     0.46 :(
## 10: 36     0.44 :(
## 11: 31     0.14 :(
## 12: 33   1e-04 ***
## 13: 32 0.00065 ***
## 14: 19 0.00033 ***
## 15: 15   0.0016 **
## 16: 10     0.18 :(
## [1] 29.2
## [1] 8.38

## [1] "medium"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.069 29     0.074 .
##  2:      0.09375          0.150 37 4.2e-05 ***
##  3:      0.15625          0.094 41   0.0054 **
##  4:      0.21875          0.072 43     0.055 .
##  5:      0.28125          0.140 45    0.005 **
##  6:      0.34375          0.081 39     0.066 .
##  7:      0.40625          0.027 45     0.62 :(
##  8:      0.46875          0.025 43     0.32 :(
##  9:      0.53125         -0.069 40   0.0071 **
## 10:      0.59375         -0.094 40   0.0062 **
## 11:      0.65625         -0.130 45   0.0032 **
## 12:      0.71875         -0.150 43 0.00052 ***
## 13:      0.78125         -0.180 43 3.5e-05 ***
## 14:      0.84375         -0.220 43 4.4e-07 ***
## 15:      0.90625         -0.240 34 1.6e-06 ***
## 16:      0.96875         -0.170 32 0.00016 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 29     0.074 .
##  2: 37 4.2e-05 ***
##  3: 41   0.0054 **
##  4: 43     0.055 .
##  5: 45    0.005 **
##  6: 39     0.066 .
##  7: 45     0.62 :(
##  8: 43     0.32 :(
##  9: 40   0.0071 **
## 10: 40   0.0062 **
## 11: 45   0.0032 **
## 12: 43 0.00052 ***
## 13: 43 3.5e-05 ***
## 14: 43 4.4e-07 ***
## 15: 34 1.6e-06 ***
## 16: 32 0.00016 ***
## [1] 40.1
## [1] 4.84

## [1] "bad"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.220  6     0.13 :(
##  2:      0.09375          0.160 15     0.048 *
##  3:      0.15625          0.280 22 0.00025 ***
##  4:      0.21875          0.170 23   0.0017 **
##  5:      0.28125          0.100 24   0.0086 **
##  6:      0.34375          0.160 24   0.0024 **
##  7:      0.40625          0.094 25     0.18 :(
##  8:      0.46875          0.045 24     0.14 :(
##  9:      0.53125         -0.031 21   0.0047 **
## 10:      0.59375         -0.044 27     0.36 :(
## 11:      0.65625         -0.140 24      0.4 :(
## 12:      0.71875         -0.170 27 0.00049 ***
## 13:      0.78125         -0.210 28   5e-05 ***
## 14:      0.84375         -0.230 29 3.5e-05 ***
## 15:      0.90625         -0.220 28 2.7e-05 ***
## 16:      0.96875         -0.310 28 3.8e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  6     0.13 :(
##  2: 15     0.048 *
##  3: 22 0.00025 ***
##  4: 23   0.0017 **
##  5: 24   0.0086 **
##  6: 24   0.0024 **
##  7: 25     0.18 :(
##  8: 24     0.14 :(
##  9: 21   0.0047 **
## 10: 27     0.36 :(
## 11: 24      0.4 :(
## 12: 27 0.00049 ***
## 13: 28   5e-05 ***
## 14: 29 3.5e-05 ***
## 15: 28 2.7e-05 ***
## 16: 28 3.8e-06 ***
## [1] 23.4
## [1] 5.81

Motor task

## [1] "all"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  0          NA
##  2:      0.09375          0.160  8     0.29 :(
##  3:      0.15625          0.094 29     0.22 :(
##  4:      0.21875          0.056 41     0.063 .
##  5:      0.28125          0.085 48      0.01 *
##  6:      0.34375          0.069 50     0.034 *
##  7:      0.40625          0.069 50     0.086 .
##  8:      0.46875          0.044 54      0.03 *
##  9:      0.53125          0.019 52     0.66 :(
## 10:      0.59375         -0.019 51     0.65 :(
## 11:      0.65625         -0.040 52     0.11 :(
## 12:      0.71875         -0.069 53   0.0037 **
## 13:      0.78125         -0.110 46 0.00082 ***
## 14:      0.84375         -0.170 29   0.0015 **
## 15:      0.90625         -0.180 13     0.056 .
## 16:      0.96875         -0.270  6     0.052 .
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  8     0.29 :(
##  2: 29     0.22 :(
##  3: 41     0.063 .
##  4: 48      0.01 *
##  5: 50     0.034 *
##  6: 50     0.086 .
##  7: 54      0.03 *
##  8: 52     0.66 :(
##  9: 51     0.65 :(
## 10: 52     0.11 :(
## 11: 53   0.0037 **
## 12: 46 0.00082 ***
## 13: 29   0.0015 **
## 14: 13     0.056 .
## 15:  6     0.052 .
## [1] 38.8
## [1] 17.3

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "good"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125             NA  0      NA
##  2:      0.09375         0.1600  8 0.29 :(
##  3:      0.15625         0.0690 24 0.45 :(
##  4:      0.21875         0.0560 25 0.15 :(
##  5:      0.28125         0.0690 25 0.071 .
##  6:      0.34375         0.0560 25 0.089 .
##  7:      0.40625         0.0810 24 0.098 .
##  8:      0.46875         0.0940 24 0.031 *
##  9:      0.53125         0.0680 23 0.21 :(
## 10:      0.59375         0.0560 22 0.28 :(
## 11:      0.65625        -0.0062 22    1 :(
## 12:      0.71875        -0.0690 21 0.11 :(
## 13:      0.78125        -0.0650 15 0.081 .
## 14:      0.84375             NA  2      NA
## 15:      0.90625             NA  0      NA
## 16:      0.96875             NA  0      NA
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1:  8 0.29 :(
##  2: 24 0.45 :(
##  3: 25 0.15 :(
##  4: 25 0.071 .
##  5: 25 0.089 .
##  6: 24 0.098 .
##  7: 24 0.031 *
##  8: 23 0.21 :(
##  9: 22 0.28 :(
## 10: 22    1 :(
## 11: 21 0.11 :(
## 12: 15 0.081 .
## [1] 21.5
## [1] 5.07

## Warning: Removed 4 rows containing missing values (geom_point).

## Warning: Removed 4 rows containing missing values (geom_errorbar).

## [1] "medium"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125             NA  0      NA
##  2:      0.09375             NA  0      NA
##  3:      0.15625          0.290  5 0.28 :(
##  4:      0.21875          0.048 16 0.22 :(
##  5:      0.28125          0.120 23  0.07 .
##  6:      0.34375          0.069 24 0.26 :(
##  7:      0.40625          0.054 25 0.45 :(
##  8:      0.46875          0.031 25 0.25 :(
##  9:      0.53125         -0.028 25 0.63 :(
## 10:      0.59375         -0.069 22 0.23 :(
## 11:      0.65625         -0.130 23 0.019 *
## 12:      0.71875         -0.069 25 0.084 .
## 13:      0.78125         -0.120 25 0.013 *
## 14:      0.84375         -0.170 20 0.024 *
## 15:      0.90625         -0.170  6 0.14 :(
## 16:      0.96875             NA  0      NA
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1:  5 0.28 :(
##  2: 16 0.22 :(
##  3: 23  0.07 .
##  4: 24 0.26 :(
##  5: 25 0.45 :(
##  6: 25 0.25 :(
##  7: 25 0.63 :(
##  8: 22 0.23 :(
##  9: 23 0.019 *
## 10: 25 0.084 .
## 11: 25 0.013 *
## 12: 20 0.024 *
## 13:  6 0.14 :(
## [1] 20.3
## [1] 7.06

## Warning: Removed 3 rows containing missing values (geom_point).

## Warning: Removed 3 rows containing missing values (geom_errorbar).

## [1] "bad"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj n    pval
##  1:      0.03125             NA 0      NA
##  2:      0.09375             NA 0      NA
##  3:      0.15625             NA 0      NA
##  4:      0.21875             NA 0      NA
##  5:      0.28125             NA 0      NA
##  6:      0.34375             NA 1      NA
##  7:      0.40625             NA 1      NA
##  8:      0.46875          0.031 5 0.58 :(
##  9:      0.53125             NA 4      NA
## 10:      0.59375         -0.050 7  0.1 :(
## 11:      0.65625         -0.019 7 0.93 :(
## 12:      0.71875         -0.120 7 0.15 :(
## 13:      0.78125         -0.130 6 0.14 :(
## 14:      0.84375         -0.170 7 0.11 :(
## 15:      0.90625         -0.200 7 0.27 :(
## 16:      0.96875         -0.270 6 0.052 .
## [1] "mean and sd of nb players per bin"
##    nb    pval
## 1:  5 0.58 :(
## 2:  7  0.1 :(
## 3:  7 0.93 :(
## 4:  7 0.15 :(
## 5:  6 0.14 :(
## 6:  7 0.11 :(
## 7:  7 0.27 :(
## 8:  6 0.052 .
## [1] 6.5
## [1] 0.756

## Warning: Removed 8 rows containing missing values (geom_point).

## Warning: Removed 8 rows containing missing values (geom_errorbar).

Sensory task

## [1] "all"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0490 32     0.069 .
##  2:      0.09375         0.1200 48   0.0073 **
##  3:      0.15625         0.0940 51     0.015 *
##  4:      0.21875         0.0310 47     0.34 :(
##  5:      0.28125         0.0020 46     0.97 :(
##  6:      0.34375        -0.0440 41     0.67 :(
##  7:      0.40625        -0.0063 44     0.77 :(
##  8:      0.46875        -0.0190 42     0.63 :(
##  9:      0.53125        -0.1600 34   0.0078 **
## 10:      0.59375        -0.2400 37 0.00021 ***
## 11:      0.65625        -0.1600 40 0.00065 ***
## 12:      0.71875        -0.2200 46 1.8e-06 ***
## 13:      0.78125        -0.2700 42 3.2e-06 ***
## 14:      0.84375        -0.2400 46 3.4e-07 ***
## 15:      0.90625        -0.2200 53 4.6e-08 ***
## 16:      0.96875        -0.1300 55 3.7e-07 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 32     0.069 .
##  2: 48   0.0073 **
##  3: 51     0.015 *
##  4: 47     0.34 :(
##  5: 46     0.97 :(
##  6: 41     0.67 :(
##  7: 44     0.77 :(
##  8: 42     0.63 :(
##  9: 34   0.0078 **
## 10: 37 0.00021 ***
## 11: 40 0.00065 ***
## 12: 46 1.8e-06 ***
## 13: 42 3.2e-06 ***
## 14: 46 3.4e-07 ***
## 15: 53 4.6e-08 ***
## 16: 55 3.7e-07 ***
## [1] 44
## [1] 6.4

## [1] "good"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125         0.0810 10 0.041 *
##  2:      0.09375         0.0062 10    1 :(
##  3:      0.15625         0.0940  9 0.81 :(
##  4:      0.21875        -0.0190  5    1 :(
##  5:      0.28125         0.0022  8 0.83 :(
##  6:      0.34375         0.0063  6 0.83 :(
##  7:      0.40625        -0.1100  7 0.55 :(
##  8:      0.46875        -0.1200  9 0.23 :(
##  9:      0.53125        -0.0430  7 0.45 :(
## 10:      0.59375        -0.2400  6 0.21 :(
## 11:      0.65625        -0.2100  5 0.28 :(
## 12:      0.71875        -0.3400  9 0.012 *
## 13:      0.78125        -0.2800  7 0.051 .
## 14:      0.84375        -0.2400  7 0.074 .
## 15:      0.90625        -0.1600  9 0.075 .
## 16:      0.96875        -0.0850 10 0.18 :(
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1: 10 0.041 *
##  2: 10    1 :(
##  3:  9 0.81 :(
##  4:  5    1 :(
##  5:  8 0.83 :(
##  6:  6 0.83 :(
##  7:  7 0.55 :(
##  8:  9 0.23 :(
##  9:  7 0.45 :(
## 10:  6 0.21 :(
## 11:  5 0.28 :(
## 12:  9 0.012 *
## 13:  7 0.051 .
## 14:  7 0.074 .
## 15:  9 0.075 .
## 16: 10 0.18 :(
## [1] 7.75
## [1] 1.73

## [1] "medium"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0190 19     0.88 :(
##  2:      0.09375         0.0900 27     0.028 *
##  3:      0.15625         0.0021 26        1 :(
##  4:      0.21875        -0.0520 24     0.46 :(
##  5:      0.28125        -0.0310 22     0.34 :(
##  6:      0.34375        -0.0570 19     0.45 :(
##  7:      0.40625        -0.0560 22     0.36 :(
##  8:      0.46875        -0.0190 23      0.7 :(
##  9:      0.53125        -0.2600 18     0.013 *
## 10:      0.59375        -0.2400 20   0.0073 **
## 11:      0.65625        -0.1600 22     0.026 *
## 12:      0.71875        -0.2200 21   0.0016 **
## 13:      0.78125        -0.1900 19      0.01 *
## 14:      0.84375        -0.2700 24 0.00018 ***
## 15:      0.90625        -0.2300 27   5e-05 ***
## 16:      0.96875        -0.0960 27   0.0023 **
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 19     0.88 :(
##  2: 27     0.028 *
##  3: 26        1 :(
##  4: 24     0.46 :(
##  5: 22     0.34 :(
##  6: 19     0.45 :(
##  7: 22     0.36 :(
##  8: 23      0.7 :(
##  9: 18     0.013 *
## 10: 20   0.0073 **
## 11: 22     0.026 *
## 12: 21   0.0016 **
## 13: 19      0.01 *
## 14: 24 0.00018 ***
## 15: 27   5e-05 ***
## 16: 27   0.0023 **
## [1] 22.5
## [1] 3.1

## [1] "bad"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  3          NA
##  2:      0.09375          0.160 11     0.035 *
##  3:      0.15625          0.270 16   0.0023 **
##  4:      0.21875          0.110 18     0.032 *
##  5:      0.28125          0.035 16     0.36 :(
##  6:      0.34375         -0.028 16        1 :(
##  7:      0.40625          0.094 15     0.067 .
##  8:      0.46875          0.031 10      0.3 :(
##  9:      0.53125         -0.031  9      0.4 :(
## 10:      0.59375         -0.170 11     0.026 *
## 11:      0.65625         -0.160 13     0.017 *
## 12:      0.71875         -0.220 16      0.01 *
## 13:      0.78125         -0.280 16   0.0013 **
## 14:      0.84375         -0.240 15   0.0029 **
## 15:      0.90625         -0.240 17   0.0018 **
## 16:      0.96875         -0.220 18 0.00032 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 11     0.035 *
##  2: 16   0.0023 **
##  3: 18     0.032 *
##  4: 16     0.36 :(
##  5: 16        1 :(
##  6: 15     0.067 .
##  7: 10      0.3 :(
##  8:  9      0.4 :(
##  9: 11     0.026 *
## 10: 13     0.017 *
## 11: 16      0.01 *
## 12: 16   0.0013 **
## 13: 15   0.0029 **
## 14: 17   0.0018 **
## 15: 18 0.00032 ***
## [1] 14.5
## [1] 2.92

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing missing values (geom_errorbar).

Logical task

## [1] "all"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.1400 38   0.0033 **
##  2:      0.09375         0.1800 43 8.1e-06 ***
##  3:      0.15625         0.2000 48 3.1e-06 ***
##  4:      0.21875         0.1900 50   2e-06 ***
##  5:      0.28125         0.2200 49 6.1e-05 ***
##  6:      0.34375         0.1800 41 2.3e-05 ***
##  7:      0.40625         0.0940 47     0.011 *
##  8:      0.46875         0.0310 47   0.0053 **
##  9:      0.53125        -0.0310 45     0.087 .
## 10:      0.59375         0.0062 46     0.86 :(
## 11:      0.65625        -0.0810 42     0.15 :(
## 12:      0.71875        -0.1700 41   0.0011 **
## 13:      0.78125        -0.1600 53 7.2e-06 ***
## 14:      0.84375        -0.2400 52 1.4e-08 ***
## 15:      0.90625        -0.2600 42 1.2e-07 ***
## 16:      0.96875        -0.3900 29 2.6e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 38   0.0033 **
##  2: 43 8.1e-06 ***
##  3: 48 3.1e-06 ***
##  4: 50   2e-06 ***
##  5: 49 6.1e-05 ***
##  6: 41 2.3e-05 ***
##  7: 47     0.011 *
##  8: 47   0.0053 **
##  9: 45     0.087 .
## 10: 46     0.86 :(
## 11: 42     0.15 :(
## 12: 41   0.0011 **
## 13: 53 7.2e-06 ***
## 14: 52 1.4e-08 ***
## 15: 42 1.2e-07 ***
## 16: 29 2.6e-06 ***
## [1] 44.6
## [1] 5.93

## [1] "good"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125          0.160 16   0.014 *
##  2:      0.09375          0.160 16   0.015 *
##  3:      0.15625          0.140 15   0.033 *
##  4:      0.21875          0.140 14    0.03 *
##  5:      0.28125          0.120 11   0.35 :(
##  6:      0.34375          0.065 13   0.44 :(
##  7:      0.40625          0.044  9   0.81 :(
##  8:      0.46875          0.031 14   0.66 :(
##  9:      0.53125         -0.031 14   0.49 :(
## 10:      0.59375         -0.094 13   0.068 .
## 11:      0.65625         -0.160 11   0.012 *
## 12:      0.71875         -0.240 11 0.0035 **
## 13:      0.78125         -0.180 15 0.0039 **
## 14:      0.84375         -0.340 12 0.0029 **
## 15:      0.90625         -0.460  7   0.022 *
## 16:      0.96875             NA  0        NA
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1: 16   0.014 *
##  2: 16   0.015 *
##  3: 15   0.033 *
##  4: 14    0.03 *
##  5: 11   0.35 :(
##  6: 13   0.44 :(
##  7:  9   0.81 :(
##  8: 14   0.66 :(
##  9: 14   0.49 :(
## 10: 13   0.068 .
## 11: 11   0.012 *
## 12: 11 0.0035 **
## 13: 15 0.0039 **
## 14: 12 0.0029 **
## 15:  7   0.022 *
## [1] 12.7
## [1] 2.58

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "medium"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.1200 18     0.18 :(
##  2:      0.09375         0.2000 21 0.00039 ***
##  3:      0.15625         0.2000 22 0.00073 ***
##  4:      0.21875         0.1900 21   0.0044 **
##  5:      0.28125         0.2200 21     0.017 *
##  6:      0.34375         0.1800 14    0.002 **
##  7:      0.40625         0.1200 20   0.0053 **
##  8:      0.46875         0.0560 17     0.027 *
##  9:      0.53125        -0.0310 16     0.81 :(
## 10:      0.59375         0.0062 17     0.78 :(
## 11:      0.65625        -0.0560 18      0.6 :(
## 12:      0.71875        -0.0440 17     0.53 :(
## 13:      0.78125        -0.1500 20     0.012 *
## 14:      0.84375        -0.2200 21 0.00044 ***
## 15:      0.90625        -0.2300 16   0.0014 **
## 16:      0.96875        -0.4700 10   0.0067 **
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 18     0.18 :(
##  2: 21 0.00039 ***
##  3: 22 0.00073 ***
##  4: 21   0.0044 **
##  5: 21     0.017 *
##  6: 14    0.002 **
##  7: 20   0.0053 **
##  8: 17     0.027 *
##  9: 16     0.81 :(
## 10: 17     0.78 :(
## 11: 18      0.6 :(
## 12: 17     0.53 :(
## 13: 20     0.012 *
## 14: 21 0.00044 ***
## 15: 16   0.0014 **
## 16: 10   0.0067 **
## [1] 18.1
## [1] 3.17

## [1] "bad"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.220  4     0.58 :(
##  2:      0.09375          0.160  6     0.29 :(
##  3:      0.15625          0.340 11     0.017 *
##  4:      0.21875          0.260 15   0.0018 **
##  5:      0.28125          0.220 17   0.0013 **
##  6:      0.34375          0.280 14   0.0011 **
##  7:      0.40625          0.094 18     0.43 :(
##  8:      0.46875          0.056 16     0.023 *
##  9:      0.53125         -0.031 15     0.032 *
## 10:      0.59375          0.081 16     0.11 :(
## 11:      0.65625          0.060 13     0.94 :(
## 12:      0.71875         -0.170 13     0.029 *
## 13:      0.78125         -0.130 18     0.016 *
## 14:      0.84375         -0.210 19   0.0017 **
## 15:      0.90625         -0.240 19 0.00074 ***
## 16:      0.96875         -0.370 19 0.00014 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  4     0.58 :(
##  2:  6     0.29 :(
##  3: 11     0.017 *
##  4: 15   0.0018 **
##  5: 17   0.0013 **
##  6: 14   0.0011 **
##  7: 18     0.43 :(
##  8: 16     0.023 *
##  9: 15     0.032 *
## 10: 16     0.11 :(
## 11: 13     0.94 :(
## 12: 13     0.029 *
## 13: 18     0.016 *
## 14: 19   0.0017 **
## 15: 19 0.00074 ***
## 16: 19 0.00014 ***
## [1] 14.6
## [1] 4.44

Influence of Playtime on Subjective Difficulty Error

For all groups, motor, sensitive and logical

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.71942  -0.17247   0.00783   0.17452   0.64036  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.168653   0.015973   10.56   <2e-16 ***
## timeNorm     0.007875   0.017892    0.44     0.66    
## obj.diff    -0.353796   0.024499  -14.44   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.05568373)
## 
##     Null deviance: 116.20  on 1880  degrees of freedom
## Residual deviance: 104.57  on 1878  degrees of freedom
## AIC: -89.41
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.78054  -0.20402  -0.04881   0.24891   0.83542  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.09780    0.01419   6.890 7.67e-12 ***
## timeNorm     0.04961    0.02071   2.395   0.0167 *  
## obj.diff    -0.35946    0.01812 -19.840  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07049446)
## 
##     Null deviance: 155.50  on 1814  degrees of freedom
## Residual deviance: 127.74  on 1812  degrees of freedom
## AIC: 341.96
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.71114  -0.19255  -0.00268   0.18201   0.72417  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.24232    0.01477  16.412  < 2e-16 ***
## timeNorm     0.05282    0.01983   2.664  0.00779 ** 
## obj.diff    -0.56482    0.01910 -29.567  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06780909)
## 
##     Null deviance: 188.61  on 1880  degrees of freedom
## Residual deviance: 127.35  on 1878  degrees of freedom
## AIC: 281.16
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean    error.diff   n    pval
##  1:      1.5      0.4438596     0.4457102  0.0017643322 342 0.89 :(
##  2:      4.5      0.5374269     0.5624859 -0.0193532613 171 0.35 :(
##  3:      7.5      0.5157895     0.5357049 -0.0159697343 171 0.41 :(
##  4:     10.5      0.5421053     0.5362058  0.0148489779 171 0.46 :(
##  5:     13.5      0.5157895     0.5133937  0.0051408312 171  0.8 :(
##  6:     16.5      0.5315789     0.5320036  0.0012773881 171 0.95 :(
##  7:     19.5      0.5064327     0.5358363 -0.0298109502 171  0.1 :(
##  8:     22.5      0.4871345     0.4961373 -0.0092826000 171 0.66 :(
##  9:     25.5      0.4888889     0.4868060 -0.0006626257 171 0.98 :(
## 10:     28.5      0.4736842     0.4657574  0.0047061353 171 0.81 :(
##     time    error.diff shapes
##  1:  1.5  0.0017643322     16
##  2:  4.5 -0.0193532613     16
##  3:  7.5 -0.0159697343     16
##  4: 10.5  0.0148489779     16
##  5: 13.5  0.0051408312     16
##  6: 16.5  0.0012773881     16
##  7: 19.5 -0.0298109502     16
##  8: 22.5 -0.0092826000     16
##  9: 25.5 -0.0006626257     16
## 10: 28.5  0.0047061353     16

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.3309091     0.3121174  0.01322078 330     0.53 :(
##  2:      4.5      0.5115152     0.6623901 -0.14555654 165 5.5e-10 ***
##  3:      7.5      0.4606061     0.5756691 -0.11526777 165 1.3e-06 ***
##  4:     10.5      0.5145455     0.6169890 -0.10480311 165 7.5e-06 ***
##  5:     13.5      0.4721212     0.5882784 -0.10892892 165 1.7e-07 ***
##  6:     16.5      0.4327273     0.5480044 -0.12509592 165   6e-07 ***
##  7:     19.5      0.4824242     0.5706900 -0.08307269 165 2.6e-05 ***
##  8:     22.5      0.5018182     0.5568448 -0.05086146 165     0.019 *
##  9:     25.5      0.5424242     0.5635905 -0.01007155 165     0.61 :(
## 10:     28.5      0.5084848     0.5525507 -0.04380158 165     0.048 *
##     time  error.diff shapes
##  1:  1.5  0.01322078     16
##  2:  4.5 -0.14555654     24
##  3:  7.5 -0.11526777     24
##  4: 10.5 -0.10480311     24
##  5: 13.5 -0.10892892     24
##  6: 16.5 -0.12509592     24
##  7: 19.5 -0.08307269     24
##  8: 22.5 -0.05086146     24
##  9: 25.5 -0.01007155     16
## 10: 28.5 -0.04380158     24

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n        pval
##  1:      1.5      0.3938596     0.3515160  0.050094933 342     0.016 *
##  2:      4.5      0.5081871     0.6513076 -0.158414133 171 7.7e-09 ***
##  3:      7.5      0.5093567     0.5682979 -0.068502095 171   0.0063 **
##  4:     10.5      0.5204678     0.5388474 -0.021601840 171     0.38 :(
##  5:     13.5      0.5157895     0.5225795 -0.010574437 171     0.66 :(
##  6:     16.5      0.5093567     0.5042410 -0.004080511 171     0.87 :(
##  7:     19.5      0.4614035     0.4415088  0.014455090 171     0.54 :(
##  8:     22.5      0.4280702     0.4078173  0.014864320 171     0.56 :(
##  9:     25.5      0.4614035     0.3856125  0.082930837 171   0.0017 **
## 10:     28.5      0.4485380     0.3513216  0.090016969 171 0.00063 ***
##     time   error.diff shapes
##  1:  1.5  0.050094933     24
##  2:  4.5 -0.158414133     24
##  3:  7.5 -0.068502095     24
##  4: 10.5 -0.021601840     16
##  5: 13.5 -0.010574437     16
##  6: 16.5 -0.004080511     16
##  7: 19.5  0.014455090     16
##  8: 22.5  0.014864320     16
##  9: 25.5  0.082930837     24
## 10: 28.5  0.090016969     24

For all taks, per group

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.74048  -0.17401  -0.02359   0.20851   0.61078  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.29441    0.01853  15.887  < 2e-16 ***
## timeNorm     0.07015    0.02141   3.276  0.00108 ** 
## obj.diff    -0.61831    0.02218 -27.872  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06153219)
## 
##     Null deviance: 137.38  on 1451  degrees of freedom
## Residual deviance:  89.16  on 1449  degrees of freedom
## AIC: 77.136
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "medium"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7526  -0.2149  -0.0081   0.2261   0.7868  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.15149    0.01334   11.36  < 2e-16 ***
## timeNorm     0.05305    0.01786    2.97  0.00301 ** 
## obj.diff    -0.40043    0.01855  -21.58  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.0718457)
## 
##     Null deviance: 208.93  on 2441  degrees of freedom
## Residual deviance: 175.23  on 2439  degrees of freedom
## AIC: 504.74
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.67435  -0.19283   0.00328   0.19952   0.80040  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.15358    0.01403   10.95   <2e-16 ***
## timeNorm     0.01374    0.01936    0.71    0.478    
## obj.diff    -0.37554    0.02259  -16.62   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.05825456)
## 
##     Null deviance: 114.051  on 1682  degrees of freedom
## Residual deviance:  97.868  on 1680  degrees of freedom
## AIC: -3.5117
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean    error.diff   n        pval
##  1:      1.5      0.4905303     0.4913488 -0.0013274152 264     0.96 :(
##  2:      4.5      0.5757576     0.7895046 -0.2242818320 132 9.7e-13 ***
##  3:      7.5      0.5871212     0.7343967 -0.1591895348 132 2.6e-08 ***
##  4:     10.5      0.6318182     0.7058964 -0.0768769607 132   0.0069 **
##  5:     13.5      0.5969697     0.6981354 -0.1232203030 132 0.00011 ***
##  6:     16.5      0.5946970     0.6912326 -0.1113715122 132 0.00024 ***
##  7:     19.5      0.5772727     0.6352225 -0.0622268548 132      0.01 *
##  8:     22.5      0.5901515     0.6448623 -0.0548194991 132      0.04 *
##  9:     25.5      0.5856061     0.6145444 -0.0298282189 132     0.36 :(
## 10:     28.5      0.5901515     0.5903874  0.0003306291 132        1 :(
##     time    error.diff shapes
##  1:  1.5 -0.0013274152     16
##  2:  4.5 -0.2242818320     24
##  3:  7.5 -0.1591895348     24
##  4: 10.5 -0.0768769607     24
##  5: 13.5 -0.1232203030     24
##  6: 16.5 -0.1113715122     24
##  7: 19.5 -0.0622268548     24
##  8: 22.5 -0.0548194991     24
##  9: 25.5 -0.0298282189     16
## 10: 28.5  0.0003306291     16

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n        pval
##  1:      1.5      0.3677928     0.3551579  0.011036580 444     0.49 :(
##  2:      4.5      0.5265766     0.6270722 -0.094465131 222 4.8e-07 ***
##  3:      7.5      0.4963964     0.5268408 -0.033741989 222     0.095 .
##  4:     10.5      0.5171171     0.5721783 -0.057803088 222   0.0067 **
##  5:     13.5      0.5166667     0.5432406 -0.027271296 222     0.15 :(
##  6:     16.5      0.4792793     0.5171087 -0.042499516 222     0.025 *
##  7:     19.5      0.4945946     0.5431912 -0.052735855 222   0.0057 **
##  8:     22.5      0.4450450     0.4701726 -0.031765890 222     0.11 :(
##  9:     25.5      0.5121622     0.4803948  0.027459136 222     0.17 :(
## 10:     28.5      0.5000000     0.4790999  0.004391434 222     0.83 :(
##     time   error.diff shapes
##  1:  1.5  0.011036580     16
##  2:  4.5 -0.094465131     24
##  3:  7.5 -0.033741989     16
##  4: 10.5 -0.057803088     24
##  5: 13.5 -0.027271296     16
##  6: 16.5 -0.042499516     24
##  7: 19.5 -0.052735855     24
##  8: 22.5 -0.031765890     16
##  9: 25.5  0.027459136     16
## 10: 28.5  0.004391434     16

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n      pval
##  1:      1.5      0.3362745     0.2883788  0.053939073 306 0.0024 **
##  2:      4.5      0.4594771     0.4799245 -0.018644423 153   0.44 :(
##  3:      7.5      0.4156863     0.4566723 -0.034184928 153   0.13 :(
##  4:     10.5      0.4470588     0.4276822  0.026506713 153   0.21 :(
##  5:     13.5      0.3973856     0.4017259  0.005755312 153   0.81 :(
##  6:     16.5      0.4215686     0.4024687  0.019843066 153    0.4 :(
##  7:     19.5      0.3862745     0.3715820  0.015027692 153   0.53 :(
##  8:     22.5      0.4091503     0.3722581  0.038296120 153   0.066 .
##  9:     25.5      0.3986928     0.3556109  0.043890398 153   0.028 *
## 10:     28.5      0.3444444     0.3045755  0.038244090 153   0.056 .
##     time   error.diff shapes
##  1:  1.5  0.053939073     24
##  2:  4.5 -0.018644423     16
##  3:  7.5 -0.034184928     16
##  4: 10.5  0.026506713     16
##  5: 13.5  0.005755312     16
##  6: 16.5  0.019843066     16
##  7: 19.5  0.015027692     16
##  8: 22.5  0.038296120     16
##  9: 25.5  0.043890398     24
## 10: 28.5  0.038244090     16

Per group, motor task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.68318  -0.17197  -0.06763   0.20953   0.44308  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.26214    0.07667   3.419 0.000745 ***
## timeNorm     0.03922    0.04490   0.873 0.383385    
## obj.diff    -0.52604    0.09657  -5.448 1.32e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.04276234)
## 
##     Null deviance: 11.0260  on 230  degrees of freedom
## Residual deviance:  9.7498  on 228  degrees of freedom
## AIC: -67.605
## 
## Number of Fisher Scoring iterations: 2

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): cannot compute exact confidence interval with ties

##     time.bin subj.diff.mean obj.diff.mean  error.diff  n      pval
##  1:      1.5      0.5928571     0.7170177 -0.13220955 42 0.0011 **
##  2:      4.5      0.6571429     0.7946114 -0.14995732 21   0.019 *
##  3:      7.5      0.6380952     0.7649789 -0.12991685 21   0.035 *
##  4:     10.5      0.6571429     0.7869246 -0.13826606 21   0.026 *
##  5:     13.5      0.6428571     0.8120284 -0.18251913 21 0.0016 **
##  6:     16.5      0.6571429     0.7887369 -0.14411936 21    0.06 .
##  7:     19.5      0.6761905     0.7250289 -0.04778299 21   0.29 :(
##  8:     22.5      0.6666667     0.7637626 -0.10072169 21    0.1 :(
##  9:     25.5      0.6809524     0.8157609 -0.13535368 21   0.013 *
## 10:     28.5      0.6333333     0.7674702 -0.11749764 21   0.042 *
##     time  error.diff shapes
##  1:  1.5 -0.13220955     24
##  2:  4.5 -0.14995732     24
##  3:  7.5 -0.12991685     24
##  4: 10.5 -0.13826606     24
##  5: 13.5 -0.18251913     24
##  6: 16.5 -0.14411936     16
##  7: 19.5 -0.04778299     16
##  8: 22.5 -0.10072169     16
##  9: 25.5 -0.13535368     24
## 10: 28.5 -0.11749764     24

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.70317  -0.16896   0.01375   0.17698   0.64802  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.18709    0.02966   6.307 4.63e-10 ***
## timeNorm    -0.01154    0.02965  -0.389    0.697    
## obj.diff    -0.38631    0.04612  -8.375 2.36e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.0670456)
## 
##     Null deviance: 59.836  on 824  degrees of freedom
## Residual deviance: 55.111  on 822  degrees of freedom
## AIC: 116.78
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n     pval
##  1:      1.5      0.4600000     0.4703926 -0.004017585 150  0.91 :(
##  2:      4.5      0.5626667     0.6181213 -0.049929670  75  0.12 :(
##  3:      7.5      0.5386667     0.5405554  0.007318205  75  0.84 :(
##  4:     10.5      0.5426667     0.5682867 -0.012295430  75  0.75 :(
##  5:     13.5      0.5506667     0.5516441 -0.004940452  75  0.87 :(
##  6:     16.5      0.5440000     0.5685882 -0.030883251  75  0.31 :(
##  7:     19.5      0.4946667     0.5794923 -0.085106719  75 0.003 **
##  8:     22.5      0.4706667     0.5231952 -0.058919282  75  0.072 .
##  9:     25.5      0.5013333     0.5079792 -0.014719847  75  0.71 :(
## 10:     28.5      0.5013333     0.5148979 -0.027308081  75  0.35 :(
##     time   error.diff shapes
##  1:  1.5 -0.004017585     16
##  2:  4.5 -0.049929670     16
##  3:  7.5  0.007318205     16
##  4: 10.5 -0.012295430     16
##  5: 13.5 -0.004940452     16
##  6: 16.5 -0.030883251     16
##  7: 19.5 -0.085106719     24
##  8: 22.5 -0.058919282     16
##  9: 25.5 -0.014719847     16
## 10: 28.5 -0.027308081     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.61865  -0.16483   0.01371   0.17764   0.56340  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.11206    0.02131   5.260 1.84e-07 ***
## timeNorm     0.02484    0.02475   1.004    0.316    
## obj.diff    -0.19452    0.03920  -4.963 8.45e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.04664526)
## 
##     Null deviance: 39.558  on 824  degrees of freedom
## Residual deviance: 38.342  on 822  degrees of freedom
## AIC: -182.53
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n      pval
##  1:      1.5      0.3860000     0.3450617  0.042653637 150   0.021 *
##  2:      4.5      0.4786667     0.4418554  0.043104906  75   0.14 :(
##  3:      7.5      0.4586667     0.4666577 -0.005389581  75   0.86 :(
##  4:     10.5      0.5093333     0.4339237  0.083907158  75 0.0024 **
##  5:     13.5      0.4453333     0.3915256  0.062218949  75   0.021 *
##  6:     16.5      0.4840000     0.4235337  0.063525179  75   0.015 *
##  7:     19.5      0.4706667     0.4392064  0.031920568  75   0.25 :(
##  8:     22.5      0.4533333     0.3941442  0.062224272  75   0.026 *
##  9:     25.5      0.4226667     0.3735255  0.050458512  75    0.05 .
## 10:     28.5      0.4013333     0.3321373  0.066488010  75 0.0078 **
##     time   error.diff shapes
##  1:  1.5  0.042653637     24
##  2:  4.5  0.043104906     16
##  3:  7.5 -0.005389581     16
##  4: 10.5  0.083907158     24
##  5: 13.5  0.062218949     24
##  6: 16.5  0.063525179     24
##  7: 19.5  0.031920568     16
##  8: 22.5  0.062224272     24
##  9: 25.5  0.050458512     16
## 10: 28.5  0.066488010     24

Per group, sensory task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.74353  -0.20874   0.00921   0.20654   0.60833  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.22508    0.02572   8.752   <2e-16 ***
## timeNorm     0.03968    0.03440   1.154    0.249    
## obj.diff    -0.51555    0.03132 -16.460   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06419126)
## 
##     Null deviance: 55.359  on 593  degrees of freedom
## Residual deviance: 37.937  on 591  degrees of freedom
## AIC: 59.634
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.4361111     0.3647578  0.07303014 108     0.039 *
##  2:      4.5      0.5592593     0.7356615 -0.18434054  54 0.00014 ***
##  3:      7.5      0.5574074     0.7147027 -0.17041673  54 0.00024 ***
##  4:     10.5      0.6314815     0.7227008 -0.08581209  54     0.047 *
##  5:     13.5      0.5351852     0.6812848 -0.14784579  54 0.00032 ***
##  6:     16.5      0.4907407     0.6163897 -0.15815934  54    0.005 **
##  7:     19.5      0.5129630     0.6057373 -0.10594419  54     0.014 *
##  8:     22.5      0.6129630     0.6322030 -0.01267083  54     0.83 :(
##  9:     25.5      0.5537037     0.5850422 -0.03200306  54     0.64 :(
## 10:     28.5      0.5611111     0.5894469 -0.01732044  54     0.63 :(
##     time  error.diff shapes
##  1:  1.5  0.07303014     24
##  2:  4.5 -0.18434054     24
##  3:  7.5 -0.17041673     24
##  4: 10.5 -0.08581209     24
##  5: 13.5 -0.14784579     24
##  6: 16.5 -0.15815934     24
##  7: 19.5 -0.10594419     24
##  8: 22.5 -0.01267083     16
##  9: 25.5 -0.03200306     16
## 10: 28.5 -0.01732044     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.74906  -0.19267  -0.05492   0.23540   0.86516  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.06264    0.02036   3.076  0.00216 ** 
## timeNorm     0.05239    0.03029   1.729  0.08410 .  
## obj.diff    -0.30669    0.02664 -11.513  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07338495)
## 
##     Null deviance: 74.897  on 890  degrees of freedom
## Residual deviance: 65.166  on 888  degrees of freedom
## AIC: 206.22
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n        pval
##  1:      1.5      0.3000000     0.3038003 -0.021976446 162     0.57 :(
##  2:      4.5      0.5086420     0.6435586 -0.118822900  81 1.9e-05 ***
##  3:      7.5      0.4296296     0.5229068 -0.099048300  81   0.0065 **
##  4:     10.5      0.4740741     0.6031879 -0.132293983  81 0.00011 ***
##  5:     13.5      0.4753086     0.5733030 -0.090316862  81   0.0029 **
##  6:     16.5      0.4333333     0.5345376 -0.094416022  81   0.0032 **
##  7:     19.5      0.5222222     0.5914733 -0.055143049  81      0.07 .
##  8:     22.5      0.4283951     0.5257793 -0.094106034  81 0.00089 ***
##  9:     25.5      0.5580247     0.5797558 -0.008270062  81     0.69 :(
## 10:     28.5      0.5160494     0.5619786 -0.059751925  81     0.048 *
##     time   error.diff shapes
##  1:  1.5 -0.021976446     16
##  2:  4.5 -0.118822900     24
##  3:  7.5 -0.099048300     24
##  4: 10.5 -0.132293983     24
##  5: 13.5 -0.090316862     24
##  6: 16.5 -0.094416022     24
##  7: 19.5 -0.055143049     16
##  8: 22.5 -0.094106034     24
##  9: 25.5 -0.008270062     16
## 10: 28.5 -0.059751925     24

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.64976  -0.15267  -0.07143   0.24828   0.73575  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.03381    0.03009   1.124    0.262    
## timeNorm     0.05708    0.04702   1.214    0.226    
## obj.diff    -0.30079    0.04072  -7.387 1.26e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06611709)
## 
##     Null deviance: 25.23  on 329  degrees of freedom
## Residual deviance: 21.62  on 327  degrees of freedom
## AIC: 45.097
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean  error.diff  n      pval
##  1:      1.5      0.2250000     0.2398207 -0.02972150 60   0.32 :(
##  2:      4.5      0.4333333     0.5813467 -0.14387812 30 0.0099 **
##  3:      7.5      0.3700000     0.4678668 -0.07002244 30    0.1 :(
##  4:     10.5      0.4133333     0.4639707 -0.06867710 30   0.17 :(
##  5:     13.5      0.3500000     0.4613004 -0.07486892 30   0.036 *
##  6:     16.5      0.3266667     0.4612711 -0.14672095 30 0.0032 **
##  7:     19.5      0.3200000     0.4514902 -0.11134531 30 0.0015 **
##  8:     22.5      0.5000000     0.5050770  0.01525165 30   0.81 :(
##  9:     25.5      0.4800000     0.4813309  0.01240702 30   0.63 :(
## 10:     28.5      0.3933333     0.4606821 -0.04485632 30   0.26 :(
##     time  error.diff shapes
##  1:  1.5 -0.02972150     16
##  2:  4.5 -0.14387812     24
##  3:  7.5 -0.07002244     16
##  4: 10.5 -0.06867710     16
##  5: 13.5 -0.07486892     24
##  6: 16.5 -0.14672095     24
##  7: 19.5 -0.11134531     24
##  8: 22.5  0.01525165     16
##  9: 25.5  0.01240702     16
## 10: 28.5 -0.04485632     16

Per group, logical task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.69105  -0.13535  -0.04275   0.22779   0.52186  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.40494    0.03034  13.348   <2e-16 ***
## timeNorm     0.08434    0.03287   2.566   0.0105 *  
## obj.diff    -0.78286    0.03556 -22.014   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06221689)
## 
##     Null deviance: 70.193  on 626  degrees of freedom
## Residual deviance: 38.823  on 624  degrees of freedom
## AIC: 43.082
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.5043860     0.5281359 -0.02386644 114     0.59 :(
##  2:      4.5      0.5614035     0.8386323 -0.29409937  57 5.7e-08 ***
##  3:      7.5      0.5964912     0.7417870 -0.16315992  57 0.00049 ***
##  4:     10.5      0.6228070     0.6601240 -0.03758210  57     0.35 :(
##  5:     13.5      0.6385965     0.6721386 -0.04541563  57     0.42 :(
##  6:     16.5      0.6701754     0.7262138 -0.06573562  57     0.17 :(
##  7:     19.5      0.6017544     0.6300694 -0.03104148  57     0.45 :(
##  8:     22.5      0.5403509     0.6130500 -0.07399401  57     0.089 .
##  9:     25.5      0.5807018     0.5683615  0.02120906  57     0.71 :(
## 10:     28.5      0.6017544     0.5260374  0.07382779  57     0.12 :(
##     time  error.diff shapes
##  1:  1.5 -0.02386644     16
##  2:  4.5 -0.29409937     24
##  3:  7.5 -0.16315992     24
##  4: 10.5 -0.03758210     16
##  5: 13.5 -0.04541563     16
##  6: 16.5 -0.06573562     16
##  7: 19.5 -0.03104148     16
##  8: 22.5 -0.07399401     16
##  9: 25.5  0.02120906     16
## 10: 28.5  0.07382779     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.66911  -0.21245  -0.01047   0.18726   0.73255  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.21616    0.02350   9.197  < 2e-16 ***
## timeNorm     0.09568    0.03223   2.968  0.00309 ** 
## obj.diff    -0.51871    0.03272 -15.853  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06935709)
## 
##     Null deviance: 68.790  on 725  degrees of freedom
## Residual deviance: 50.145  on 723  degrees of freedom
## AIC: 127.97
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n        pval
##  1:      1.5      0.3462121     0.2872390  0.065744364 132     0.026 *
##  2:      4.5      0.5075758     0.6170103 -0.114789677  66   0.0029 **
##  3:      7.5      0.5303030     0.5160840 -0.001877928  66     0.95 :(
##  4:     10.5      0.5409091     0.5385434 -0.010086501  66     0.78 :(
##  5:     13.5      0.5287879     0.4967963  0.029831439  66     0.39 :(
##  6:     16.5      0.4621212     0.4372192  0.008701097  66     0.83 :(
##  7:     19.5      0.4606061     0.4426848  0.012862790  66     0.75 :(
##  8:     22.5      0.4363636     0.3416750  0.098428785  66     0.022 *
##  9:     25.5      0.4681818     0.3271059  0.140708998  66 0.00042 ***
## 10:     28.5      0.4787879     0.3367055  0.140651590  66    0.003 **
##     time   error.diff shapes
##  1:  1.5  0.065744364     24
##  2:  4.5 -0.114789677     24
##  3:  7.5 -0.001877928     16
##  4: 10.5 -0.010086501     16
##  5: 13.5  0.029831439     16
##  6: 16.5  0.008701097     16
##  7: 19.5  0.012862790     16
##  8: 22.5  0.098428785     24
##  9: 25.5  0.140708998     24
## 10: 28.5  0.140651590     24

## Warning: Removed 2 rows containing missing values (geom_errorbar).

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.56781  -0.22714   0.01834   0.20931   0.72500  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.24885    0.02489   9.998   <2e-16 ***
## timeNorm    -0.06564    0.03567  -1.840   0.0663 .  
## obj.diff    -0.61160    0.03998 -15.297   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06046989)
## 
##     Null deviance: 45.924  on 527  degrees of freedom
## Residual deviance: 31.747  on 525  degrees of freedom
## AIC: 22.029
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean   error.diff  n    pval
##  1:      1.5      0.3281250     0.2301605  0.129831089 96 0.012 *
##  2:      4.5      0.4458333     0.4760184 -0.041549914 48 0.49 :(
##  3:      7.5      0.3770833     0.4340736 -0.050197677 48 0.23 :(
##  4:     10.5      0.3708333     0.3952495 -0.019512816 48 0.73 :(
##  5:     13.5      0.3520833     0.3804299 -0.031757187 48 0.49 :(
##  6:     16.5      0.3833333     0.3328032  0.048222443 48 0.33 :(
##  7:     19.5      0.2958333     0.2159761  0.076123999 48 0.084 .
##  8:     22.5      0.2833333     0.2550491  0.009652816 48 0.73 :(
##  9:     25.5      0.3104167     0.2490444  0.067419509 48 0.23 :(
## 10:     28.5      0.2250000     0.1639436  0.043708146 48 0.36 :(
##     time   error.diff shapes
##  1:  1.5  0.129831089     24
##  2:  4.5 -0.041549914     16
##  3:  7.5 -0.050197677     16
##  4: 10.5 -0.019512816     16
##  5: 13.5 -0.031757187     16
##  6: 16.5  0.048222443     16
##  7: 19.5  0.076123999     16
##  8: 22.5  0.009652816     16
##  9: 25.5  0.067419509     16
## 10: 28.5  0.043708146     16

Link between d(t-1) and error

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ est.confidence.norm, 
##     data = DTM)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.82635  -0.18617   0.00915   0.18749   0.74346  
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)  
## (Intercept)          0.01651    0.01130   1.462   0.1440  
## est.confidence.norm -0.04691    0.02004  -2.341   0.0193 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06166035)
## 
##     Null deviance: 116.20  on 1880  degrees of freedom
## Residual deviance: 115.86  on 1879  degrees of freedom
## AIC: 101.36
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ est.confidence.norm, 
##     data = DTS)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.91528  -0.19153   0.00036   0.15203   0.97111  
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         -0.06361    0.01379  -4.613 4.25e-06 ***
## est.confidence.norm -0.01256    0.02323  -0.541    0.589    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.08575694)
## 
##     Null deviance: 155.50  on 1814  degrees of freedom
## Residual deviance: 155.48  on 1813  degrees of freedom
## AIC: 696.67
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ est.confidence.norm, 
##     data = DTL)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.97361  -0.21791  -0.03643   0.23892   0.92021  
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)  
## (Intercept)          0.03505    0.01483   2.363   0.0182 *
## est.confidence.norm -0.05646    0.02589  -2.181   0.0293 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1001246)
## 
##     Null deviance: 188.61  on 1880  degrees of freedom
## Residual deviance: 188.13  on 1879  degrees of freedom
## AIC: 1013.2
## 
## Number of Fisher Scoring iterations: 2

## Linear mixed model fit by REML t-tests use Satterthwaite approximations
##   to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
##    Data: DTAll
## 
## REML criterion at convergence: 1420.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4944 -0.6644 -0.0047  0.6469  4.3628 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.01073  0.1036  
##  Residual             0.07329  0.2707  
## Number of obs: 5577, groups:  IDjoueur, 58
## 
## Fixed effects:
##                       Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)          3.999e-03  1.583e-02  8.900e+01   0.253 0.801141    
## est.confidence.norm -4.890e-02  1.455e-02  5.467e+03  -3.361 0.000782 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## est.cnfdnc. -0.457

## Linear mixed model fit by REML t-tests use Satterthwaite approximations
##   to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
##    Data: DTM
## 
## REML criterion at convergence: -691.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.2105 -0.6953 -0.0188  0.7233  3.1576 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.02599  0.1612  
##  Residual             0.03662  0.1914  
## Number of obs: 1881, groups:  IDjoueur, 57
## 
## Fixed effects:
##                       Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)           -0.02102    0.02488   90.70000  -0.845    0.400
## est.confidence.norm    0.03031    0.02465 1796.40000   1.229    0.219
## 
## Correlation of Fixed Effects:
##             (Intr)
## est.cnfdnc. -0.482

## Linear mixed model fit by REML t-tests use Satterthwaite approximations
##   to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
##    Data: DTS
## 
## REML criterion at convergence: 577.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0934 -0.6742  0.0414  0.6087  3.9364 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.01013  0.1006  
##  Residual             0.07607  0.2758  
## Number of obs: 1815, groups:  IDjoueur, 55
## 
## Fixed effects:
##                      Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)          -0.08917    0.02229 146.70000  -4.001 9.97e-05 ***
## est.confidence.norm   0.03712    0.03197 634.90000   1.161    0.246    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## est.cnfdnc. -0.738

## Linear mixed model fit by REML t-tests use Satterthwaite approximations
##   to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
##    Data: DTL
## 
## REML criterion at convergence: 832.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9937 -0.7008 -0.0459  0.6928  3.6148 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.01624  0.1274  
##  Residual             0.08543  0.2923  
## Number of obs: 1881, groups:  IDjoueur, 57
## 
## Fixed effects:
##                       Estimate Std. Error         df t value Pr(>|t|)  
## (Intercept)           -0.02690    0.02503  141.90000  -1.075   0.2842  
## est.confidence.norm    0.06773    0.03449 1078.00000   1.964   0.0498 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## est.cnfdnc. -0.688

## Linear mixed model fit by REML t-tests use Satterthwaite approximations
##   to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
##    Data: DTAll
## 
## REML criterion at convergence: 1420.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4944 -0.6644 -0.0047  0.6469  4.3628 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.01073  0.1036  
##  Residual             0.07329  0.2707  
## Number of obs: 5577, groups:  IDjoueur, 58
## 
## Fixed effects:
##                       Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)          3.999e-03  1.583e-02  8.900e+01   0.253 0.801141    
## est.confidence.norm -4.890e-02  1.455e-02  5.467e+03  -3.361 0.000782 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## est.cnfdnc. -0.457

## Linear mixed model fit by REML t-tests use Satterthwaite approximations
##   to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
##    Data: DTM
## 
## REML criterion at convergence: -691.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.2105 -0.6953 -0.0188  0.7233  3.1576 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.02599  0.1612  
##  Residual             0.03662  0.1914  
## Number of obs: 1881, groups:  IDjoueur, 57
## 
## Fixed effects:
##                       Estimate Std. Error         df t value Pr(>|t|)
## (Intercept)           -0.02102    0.02488   90.70000  -0.845    0.400
## est.confidence.norm    0.03031    0.02465 1796.40000   1.229    0.219
## 
## Correlation of Fixed Effects:
##             (Intr)
## est.cnfdnc. -0.482

## Linear mixed model fit by REML t-tests use Satterthwaite approximations
##   to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
##    Data: DTS
## 
## REML criterion at convergence: 577.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0934 -0.6742  0.0414  0.6087  3.9364 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.01013  0.1006  
##  Residual             0.07607  0.2758  
## Number of obs: 1815, groups:  IDjoueur, 55
## 
## Fixed effects:
##                      Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)          -0.08917    0.02229 146.70000  -4.001 9.97e-05 ***
## est.confidence.norm   0.03712    0.03197 634.90000   1.161    0.246    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## est.cnfdnc. -0.738

## Linear mixed model fit by REML t-tests use Satterthwaite approximations
##   to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
##    Data: DTL
## 
## REML criterion at convergence: 832.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9937 -0.7008 -0.0459  0.6928  3.6148 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.01624  0.1274  
##  Residual             0.08543  0.2923  
## Number of obs: 1881, groups:  IDjoueur, 57
## 
## Fixed effects:
##                       Estimate Std. Error         df t value Pr(>|t|)  
## (Intercept)           -0.02690    0.02503  141.90000  -1.075   0.2842  
## est.confidence.norm    0.06773    0.03449 1078.00000   1.964   0.0498 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## est.cnfdnc. -0.688

Subjective difficulty and play habits

Playing video game in general and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.14956, p-value = 0.8811
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##          tau 
## -0.008949093

Playing board game in general and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.4555, p-value = 0.01407
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.1448659 
## 
## [1] "pbg.on.error -0.14 0.014 *"

In game level and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.3326, p-value = 0.1827
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.06943578

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.5094, p-value = 0.6105
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.04636591

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.48639, p-value = 0.6267
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.04511785

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.4731, p-value = 0.1407
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1340852

Sex and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.8222, p-value = 0.00477
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1793215 
## 
## [1] "sexe.on.error 0.18 0.0048 **"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.6928, p-value = 0.09049
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1863138 
## 
## [1] "sexe.on.error.m 0.19 0.09 ."

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.6144, p-value = 0.1064
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1809973

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.5451, p-value = 0.1223
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1700543

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  B and A
## W = 3700, p-value = 0.05818
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.00174298  0.07072221
## sample estimates:
## difference in location 
##             0.03823549 
## 
## [1] "sexe.on.error.2 0.038 0.058 . mean(A): -0.038 mean(B): 0.001"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 431, p-value = 0.2418
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.02759275  0.10536605
## sample estimates:
## difference in location 
##             0.04159407

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 393, p-value = 0.2888
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.02906997  0.09784528
## sample estimates:
## difference in location 
##             0.03658899

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 409, p-value = 0.3268
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.03067049  0.09164194
## sample estimates:
## difference in location 
##             0.03311583

Risk aversion and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.20783, p-value = 0.8354
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.01175452

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.049556, p-value = 0.9605
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##          tau 
## -0.004872842

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.066909, p-value = 0.9467
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## 0.006667408

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.32559, p-value = 0.7447
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.03198445

Self efficacy and subjective difficulty error

## Warning: Removed 84 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.9186, p-value = 0.003516
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2241957 
## 
## [1] "self.eff.on.error -0.22 0.0035 **"

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning: Removed 29 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.6067, p-value = 0.1081
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2186683

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning: Removed 27 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.686, p-value = 0.09179
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2294667 
## 
## [1] "self.eff.on.error -0.23 0.092 ."

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning: Removed 28 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.6779, p-value = 0.09338
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2245285 
## 
## [1] "self.eff.on.error -0.22 0.093 ."

{r plot.subjective.objective.difficulty.confidence.scale, echo=FALSE} # #-------------------------------------------------------------------------------------- # # SHOWING SUBJECTIVE VS OBJECTIVE DIFFICULTY (CONFIDENCE SCALE APPROACH) # #-------------------------------------------------------------------------------------- # # plot.subjective.difficulty <- function(DT,selGroup,title){ # # print(selGroup) # # # Lien entre mise normalisée et difficultée estimée (hard / easy effect) # obj.diff.quants = seq(0,1,1/16)#quantile(DT$obj.diff, probs=(seq(0,1,0.05))) # nb.bins = length(obj.diff.quants)-1 # subj.diff.med = numeric(nb.bins) # obj.diff.bin = numeric(nb.bins) # obj.diff.bin.cur = 0; # test.pvals = numeric(nb.bins) # conf.min = numeric(nb.bins) # conf.max = numeric(nb.bins) # nb.vals = numeric(nb.bins) # shapes = numeric(nb.bins) # delta.obj.subj = numeric(nb.bins) # hist(DT$obj.diff) # for(i in 1:nb.bins){ # #obj.diff.bin.cur = round(i/10,1) # #subj.diff = DT[round(obj.diff,1)==obj.diff.bin.cur]$subj.diff.mise # obj.diff.bin.cur = (obj.diff.quants[i] + obj.diff.quants[i+1])/2.0 # #subj.diff = DT[obj.diff > obj.diff.quants[i] & obj.diff<=obj.diff.quants[i+1]]$subj.diff.mise # DTLoc = DT[obj.diff > obj.diff.quants[i] & obj.diff<=obj.diff.quants[i+1]] # if(selGroup != "all") # DTLoc = DTLoc[niveau.group==selGroup] # DTLoc = DTLoc[,.(confiance.mean=mean(subj.diff.confiance)),by=IDjoueur] # subj.diff = DTLoc$confiance.mean # obj.diff.bin[i] = obj.diff.bin.cur # subj.diff.med[i] = NA # test.pvals[i] = NA # conf.min[i] = NA # conf.max[i] = NA # delta.obj.subj[i] = NA # shapes[i] = 16 # nb.vals[i] = length(subj.diff) # if(nb.vals[i] > 1){ # try.res = try(test.res <- wilcox.test(subj.diff,mu = obj.diff.bin.cur,conf.int=T)) # if (class(try.res) != "try-error"){ # #print(test.res) # #hist(subj.diff) # test.pvals[i] = format.pval.stars(test.res$p.value) # if(test.res$p.value < 0.05) # shapes[i] = 24 # #subj.diff.med[i] = mean(subj.diff) # subj.diff.med[i] = test.res$estimate # conf.min[i] = test.res$conf.int[1] # conf.max[i] = test.res$conf.int[2] # delta.obj.subj[i] = signif(subj.diff.med[i] - obj.diff.bin.cur,digit=2) # } # } # } # # #print table of pvalues # print(data.table(obj.diff.bin=obj.diff.bin,delta.obj.subj=delta.obj.subj,n=nb.vals,pval=test.pvals)) # # #summary # print("mean and sd of nb players per bin") # DTNbVals = data.table(nb = nb.vals, pval=test.pvals) # print(DTNbVals[!is.na(pval)]) # print(signif(mean(DTNbVals[!is.na(pval)]$nb),digits=3)) # print(signif(sd(DTNbVals[!is.na(pval)]$nb),digits=3)) # # #kernel smooth # subj.diff.smooth <- ksmooth(x=DT$obj.diff,y=DT$subj.diff.confiance,bandwidth = 0.2) # DTSmooth = data.table(x=subj.diff.smooth$x,y=subj.diff.smooth$y) # # DTPlot = data.table(obj.diff=obj.diff.bin,subj.diff=subj.diff.med, shapes=shapes) # # p = ggplot() + ggtitle(title) + # # geom_line(aes(x=DTPouet$x,y=DTPouet$y))+ # geom_point(aes(x=DTPlot$obj.diff,y=DTPlot$subj.diff),alpha = 1, size = 3, shape=DTPlot$shapes) + # xlim(0,1)+ # ylim(0,1)+ # geom_errorbar(aes(x=DTPlot$obj.diff, ymin=conf.min, ymax=conf.max), width=.01,color="red") + # geom_abline(intercept = 0, slope = 1, color="blue") + # xlab("Objective Difficulty") + ylab("Subjective Difficulty") + theme(text = element_text(size=15)) # # print(p) # } #

All tasks

{r plot.subjective.difficulty.all.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTAll,"all", "All tasks, all groups") # plot.subjective.difficulty(DTAll,"good", "All tasks, good") # plot.subjective.difficulty(DTAll,"medium", "All tasks, medium") # plot.subjective.difficulty(DTAll,"bad", "All tasks, bad") #

Motor task

{r plot.subjective.difficulty.motor.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTM,"all", "Motor, all") # plot.subjective.difficulty(DTM,"good", "Motor, good") # plot.subjective.difficulty(DTM,"medium", "Motor, medium") # plot.subjective.difficulty(DTM,"bad", "Motor, bad") #

Sensory task

{r plot.subjective.difficulty.sensory.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTS,"all","Sensory, all") # plot.subjective.difficulty(DTS,"good","Sensory, good") # plot.subjective.difficulty(DTS,"medium","Sensory, medium") # plot.subjective.difficulty(DTS,"bad","Sensory, bad") #

Logical task

{r plot.subjective.difficulty.logical.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTL,"all","Logical, all") # plot.subjective.difficulty(DTL,"good","Logical, good") # plot.subjective.difficulty(DTL,"medium","Logical, medium") # plot.subjective.difficulty(DTL,"bad","Logical, bad") #